[502] OPINION
WILLIAM J. MARTINI, UNITED STATES DISTRICT JUDGE.
Plaintiffs are urethane purchasers who accuse Defendant Dow Chemical Company (“Dow”) of conspiring with others to fix the prices of urethanes. At trial, Plaintiffs intend to call a damages expert, Dr. Leslie Marx, who plans to testify regarding econometric regression models developed for this litigation. According to Plaintiffs, the regression models show what urethane prices would have been had there been no conspiracy. The difference between the actual and “but for” prices represents the measure of Plaintiffs’ damages.
Dow now moves to exclude Dr. Marx’s testimony pursuant to Federal Rule of Evidence 702 and Daubert v. Merrell Dow Phanns., Inc., 509 U.S. 579, 589, 113 S.Ct. 2786, 125 L.Ed.2d 469 (1993). In connection with Dow’s motion, the Court held a Daubert hearing on January 13, 2016. For the reasons that follow, Dow’s motion is DENIED, subject to the Court reserving on whether Dr. Marx will be permitted to opine that the variances detected in her models are attributable to the alleged conspiracy or are otherwise consistent with the evidence of the conspiracy as a whole.
I. BACKGROUND
Plaintiffs accuse Dow of conspiring with others to issue simultaneous price announcements in order to artificially inflate urethane prices. At issue in this opinion is the proposed testimony of Plaintiffs’ expert, Dr. Marx. Initially, Plaintiffs enlisted a different expert — Dr. Matthew Raiff — to testify in connection with this lawsuit. However, intervening circumstances required Plaintiffs to substitute Dr. Marx for Dr. Raiff. On August 13, 2013, the MDL Court issued an order stating that “Dr. Marx, the new expert, will not be permitted to develop her own opinions or methodologies, but must endorse and- defend Dr. Raiffs opinions.” Mem. & Order, Aug. 13, 2013, at 2, 04-md-1616 (D.Kansas), ECF No. 2974. Accordingly, on September 20, 2013, Dr. Marx filed an expert report that evaluated Dr. Raiffs methodologies and ultimately concluded that his methodologies were sound. In her report, Dr. Marx furthér indicated that she is “readily able to sponsor and defend Dr. Raiffs methodologies, opinions, and conclusions at trial.” Marx Rpt. at ¶ 12.
Dr. Marx intends to provide testimony regarding a regression analysis Dr. Raiff performed in connection with this case. Generally, the regression analysis purports to show what the prices of three types of polyether polyol products “PPPs” (MDI, polyol, and TDI 80/20) would have been but for the price-fixing conspiracy. Therefore, there are separate regression models for MDI, polyols, and TDI 80/20. See Rev. Raiff Reply, at ¶ 1. In connection with this analysis, Dr. Raiff, and later Dr. Marx, were instructed to assume the existence of a conspiracy. Marx Dep. at 217:1-19.
Dr. Raiffs regression models can be characterized as “forecasting” models or models that are predictive in nature. The models initially base themselves off data derived from a period when there was no conspiracy, known as the “benchmark period.” Next, the models are run across the conspiracy period in order to show what PPP prices would have been had there been no conspiracy. The difference between the “but for” prices and actual prices during the conspiracy measures the extent of Plaintiffs’ damages. See Pis. Opp’n at 10-14.
As stated above, the first step in conducting a regression analysis is to formulate a pricing model based on a non-eollu-sive benchmark period. The non-collusive period serves as the benchmark because it captures normal supply, demand, and cost factors untainted by any conspiracy. In other words, it serves as the control group [503] in Dr. Raiffs regression analysis. Pis. Opp’n at 17.
When formulating his forecasting models, Dr. Raiff was required to select explanatory variables to incorporate into the applicable regression equations. Rev. Raiff Rpt. ¶ 245. Those variables included, among other things, treasury rates, wages, and costs of various chemicals. Id., Fig. 44. Dr. Raiff selected variables after “studying the urethanes industry” and identifying “key economic factors for predicting urethanes prices.” Rev. Raiff Reply at 61. According to Dr. Raiff, the variables are based on “economic judgment and well-established econometric criteria.” Rev. Raiff Rpt. at ¶ 274. Dr. Raiff also supplemented his variable selection process with other statistical modeling tools that the Court will address later in this opinion. See Rev. Raiff Reply, at ¶ 62.
Next, Dr. Raiff was required to assign an appropriate coefficient to each selected variable. According to Dr. Raiff and Dr. Marx, the coefficients for each variable are harmonized in a manner so that the models show how the variables collectively impact PPP prices. Rev. Raiff Reply at ¶¶ 92, 93; Marx Rpt. ¶ 66. Therefore, when selecting coefficients, Dr. Raiff assessed how all of the selected variables operated as a whole. See id.
Once Dr. Raiff was confident that his regression models could accurately predict PPP prices during a given time period, his next task was to apply the models to the conspiracy period, January 1994 through December 2003. See, e.g., Rev. Raiff Rpt. at ¶273. Dr. Raiff accomplished this by inputting both the actual values for his December 1993 cost and demand variables along with the actual December 1993 price of the relevant PPP. This step generated the predicted PPP price for January 1994. Id. at ¶ 280. Dr. Raiff then repeated this process in order to obtain the predicted price for February 1994, but used the predicted January 1994 price instead of the actual January 1994 price. Id. He then ran this process along the entirety of the conspiracy period, and then through the post conspiracy period of January 2004 to December 2008. In other words, actual PPP prices for December 2003 were used to get the model off and running-after that, prices were forecasted based on how the model was constructed. See id.
After running the models along the conspiracy period, Dr. Raiff compared the predicted prices with the actual prices. The difference between the two represents what prices would have been in a normal, conspiracy-free environment. Where actual prices exceed but for prices, there is an overcharge. See Marx Rule 26 Disclosure at ¶¶ 41.
Dr. Raiffs regression analysis takes another step by seeking to predict prices for every urethanes transaction between Plaintiffs and Defendants. Rev. Raiff Rpt. ¶¶ 283-288. These “transaction-level models” seek to calculate damages for each individual Plaintiff by analyzing individual transactions. Id. at 283. Unlike the market-wide models, the transaction-level models seek to take into account “idiosyncratic factors” unique to the individual Plaintiffs. Id. at 287. “Such factors include (but are not necessarily limited to) the identity of the customer and the nature of the relationship (and contract, if any) between the customer and the vendor.” Id. The calculated overcharges from these models represent the extent of each Plaintiffs damages. See id.
II. DISCUSSION
Federal Rule of Evidence 702 governs the admissibility of expert testimony. It provides the following:
If scientific, technical, or other specialized knowledge will assist the trier of fact to understand the evidence or to [504] determine a fact in issue, a witness qualified as an expert by knowledge, skill, experience, training, or education, may testify thereto in the form of an opinion or otherwise, if (1) the testimony is based upon sufficient facts or data, (2) the testimony is the product of reliable principles and methods, and (3) the witness has applied the principles and methods reliably to the facts of the case.
Therefore, under Federal Rule of Evidence 702, expert testimony will be admissible only if it is both relevant and reliable. Daubert v. Merrell Dow Pharms., Inc., 509 U.S. 579, 589, 113 S.Ct. 2786, 125 L.Ed.2d 469 (1993). The Supreme Court has held that when determining whether expert testimony is reliable, courts may consider (1) whether a theory or technique “can be (and has been) tested;” (2) “whether the theory or technique has been subject to peer review or publication;” (3) “the known potential rate of error;” and (4) whether there is “general acceptance” in the methodology in the relevant scientific community. Daubert, 509 U.S. at 593-94, 113 S.Ct. 2786. However, “[t]he factors drawn from Daubert ... are neither exhaustive nor applicable in every case.” Pineda v. Ford Motor Co., 520 F.3d 237, 248 (3d Cir.2008) (citations and quotations omitted).
In serving the “gatekeeper function” and assessing the reliability of an expert’s methodology, the Court must be mindful that in order to be admissible, a scientific method need not be the “best” method or one that is demonstrably correct. “Rather, the test is whether the ‘particular opinion is based on valid reasoning and reliable methodology.’ ” Oddi v. Ford Motor Co., 234 F.3d 136, 145-46 (3d Cir.2000) (citing Kannankeril v. Terminix International Inc., 128 F.3d 802, 806 (3d Cir.1997)); see also Daubert, 509 U.S. at 588, 113 S.Ct. 2786 (Rule 702 embraces the “liberal thrust” of the Federal Rules of Evidence). Accordingly, “[vigorous cross-examination, presentation of contrary evidence, and careful instruction on the burden of proof are traditional and appropriate means of attacking shaky but admissible evidence.” Daubert, 509 U.S. at 596, 113 S.Ct. 2786. The proponent of expert evidence must demonstrate its admissibility by a preponderance of the evidence. Id. at 593 n. 10.
Dow does not appear to challenge Dr. Marx’s qualifications as an expert. Pis.’ Opp’n at 1. Instead, Dow argues that Dr. Marx must be excluded because (1) her regression models are unreliable and (2) her testimony does not fit to the facts of this case.
A. Reliability
Dow attacks of the reliability of the models on multiple fronts. Before addressing those arguments, however, the Court will first decide whether the type of regression model at issue here is generally reliable. As Plaintiffs correctly note, there are an abundance of judicial decisions supporting the premise that regression models can be a reliable tool for measuring damages in an antitrust case. See, e.g., Sun Microsystems Inc. v. Hynix Semiconductor, Inc., 608 F.Supp.2d 1166 (N.D.Cal.2009); In re LinerBoard Antitrust Litig., 497 F.Supp.2d 666, 681 (E.D.Pa.2007); In re Vitamins Antitrust Litig., No. 99-MC-00179, MDL No. 1285 (D.D.C. Mar. 20-21, 2003). However, Dow argues that Plaintiffs’ regression models are unreliable because they are “predictive” rather than “structural.” In other words, Dow argues the models are unreliable because they do not explain how the independent or explanatory variables impact the dependent variable. Dow Mot. at 18-19. The fact that Plaintiffs’ models are predictive rather than structural, however, does not render Dr. Marx’s testimony inadmissible.1 First, as explained above, [505] predictive models have withstood Daubert challenges in the past. Second, Dow offers nothing to rebut Dr. Marx’s testimony that the scientific community has accepted the type of predictive regression used in this case. See Daubert Hr’g, January 13, 2016, at 66:1-4. Finally, even if Dow were correct that a structural model may be preferable to the predictive or “reduced form” model used by Dr. Marx, exclusion would still not be warranted. See Lentz v. Mason, 32 F.Supp.2d 733, 746 (D.N.J.1999) (“For [expert] testimony to be reliable, and thus, admissible under Daubert, [the expert] need not have used the best method available, only a - reasonable one.”). Here, the type of predictive models used by Dr. Marx are generally reliable.
However, as Dow correctly points out, “the devil is in the details.” No two antitrust cases are the same, and no two regression models are the same. Accordingly, Dr. Marx’s regression models must stand on their own two feet. Dow asserts several reasons for why the specific models at issue here are unreliable. The Court now addresses those arguments.
i. Ovetfit
Dow argues that the models in this case were able to accurately match prices during the benchmark period not because they are reliable, but because they are “overfit.” A regression model is overfit where it relies on nuances that exist only during the sample benchmark period such that it fails to “accurately captur[e] the underlying economic forces that actually drive prices across all time periods.” Dow Mot. at 26. In other words, Dow argues that Dr. Raiffs regression models are so finely tuned to the benchmark period that they are unable to capture underlying economic realities that impacted prices during the conspiracy period. See id.
Dow argues that the models are overfit because they use variables that are applied exclusively to post-conspiracy years. Dow further argues that the models are fatally flawed because they fail to properly account for capacity and capacity utilization issues that impacted urethane prices during the conspiracy period. In further support of its argument regarding overfit, Dow points out that Dr. Raiff failed to conduct a “hold-out test” on his regression models. Id. at 27. For the reasons stated below, the Court rejects Dow’s position.
First, while Dow’s argument regarding overfit may go to the weight of Dr. Marx’s testimony, it does not render the testimony inadmissible under Daubert. The Court concludes that Dr. Raiff took adequate steps to improve the models’ predictive ability outside the benchmark period. Specifically, Dr. Raiff sought to avoid overfit by using the Akaike Information Criterion (“AIC”), a statistical modeling measure designed “to improve predictions, while at the same time avoiding the problem of ‘overfitting’ the model to the data.... ” Rev. Raiff Reply at ¶¶ 62-63. Here, these objective AIC variables supplemented the variables chosen by Dr. Raiff so that the model could more reliably predict out of sample data. See id. Other economists have endorsed this approach as a means to avoiding overfit. See Halbert White et al., The Measurement of Economic Damages in Antitrust Litigation, 6 ABA Antitrust L. Eeon. Committee Newsl. 17, 21 (Spring 2006) (“Objective statistical criteria are used to determine the inclusion or exclusion of legitimate predictors .... [and] to improve the forecast accuracy of the econometric model during the benchmark period.”)
Second, the decision to exclude capacity variables from the model is not grounds [506] for exclusion. The fact that Dr. Raiff did not include a specific capacity variable does not mean that capacity was unaccounted for in his regression models. Dr. Marx explains that other variables present in the regression model — namely, supply and demand factors — indirectly capture capacity decisions and their role in urethane pricing. See, e.g., Marx Rpt. at ¶ 54.2 Dr. Marx also explains that capacity and capacity utilization are excluded as express variables because they are factors that Dow and other PPP suppliers may have manipulated in furtherance of the alleged conspiracy.
Q. Let’s talk briefly about capacity. Why was capacity not included?
A. Dr. Raiff explained this in his report, and it’s commonly understood. In trying to develop a model that’s going to tell you what prices would have been in the absence of the conspiracy, you wouldn’t want to include variables that were manipulated by or affected by the conspiracy. You wouldn’t be able to get an estimate of what prices would be in the absence of the conspiracy if you’re including things that were manipulated by the conspiracy.
See Daubert Hr’g, January 13, 2016, at 74:16-25. For the reasons explained by Dr. Marx, other courts have declined to fault a regression model for excluding variables that could have been manipulated by a defendant in furtherance of an antitrust conspiracy. See Resco Prods., Inc. v. Bosai Minerals Grp., Civ.A. No. 06-235, 2015 WL 5521768, *8 (W.D.Pa. Sept. 18, 2015); In re LinerBoard Antitrust Litig., 497 F.Supp.2d at 681. The Court therefore rejects the position that an express capacity variable is a prerequisite for admissibility under Daubert.
Third, the models’ use of dummy variables for the post-conspiracy period does not render Dr. Marx’s testimony inadmissible. The dummy variables Dr. Raiff included in his regression models are narrowly tailored to account for three extraordinary events that' occurred during that time frame: Hurricane Rita; Hurricane Katrina, and consolidation in the TDI industry. See Raiff Report ¶¶ 262, 265. Dow admits that these were significant events in the context of the urethanes industry, see Dow Mot. at 29, and common sense would dictate that such seismic events would necessitate the use of unique variables to capture effects on pricing. Moreover, Dow does little to explain why the presence of dummy variables in the post-conspiracy period impacts the models’ predictive ability during the conspiracy period. Dow also fails to point out any events of similar magnitude taking place during the conspiracy period that would similarly require the implementation of unique variables. The Court therefore rejects the notion that the models’ use of dummy variables renders Dr. Marx’s testimony inadmissible.
Finally, and contrary to Dow’s position, Plaintiffs were not required to conduct a holdout test on their models in order for Dr. Marx’s testimony to be admissible. According to Dow, a hold-out test involves “holding] out a portion of the data when the model is being specified and estimated, and then run[ning] the model to see if it can predict the data that was held out.” Id. at 27. If the model can accurately predict the held out data, it may be reliable. See id. Dow’s own expert contends that Plaintiffs’ models are unreliable because, they repeatedly flunked the above-described hold-out tests. See id. at 33. [507] One factor considered when applying Daubert is whether an expert’s hypothesis “can be (and has been) tested.” In re Paoli R.R. Yard PCB Litig., 35 F.3d 717, 742 (3d Cir.1994) (citing Daubert, 509 U.S. at 593, 113 S.Ct. 2786). While it is true that Dr. Raiff did not perform the type of holdout test described by Dow,3 the models were nonetheless subjected to different tests designed to measure predicative ability. Specifically, Dr. Raiff continued to run the model as the alleged conspiracy period ended and the post-conspiracy period began. Dr. Raiff notes that as the models continue to run, they accurately predict prices during the conspiracy period, thereby proving the models’ reliability. See Rev. Raiff Reply at ¶ 38. This approach is consistent with econometric principles regarding price-fixing conspiracies. See White, The Measurement of Economic Damages in Antitrust Litigation, 6 ABA Antitrust L. Econ. Committee Newsl. at 21 (“After the cartel ceases operations ... we would expect an accurate dynamic forecast to align with what actually occurred, even though there is nothing anchoring the prediction to the actual price level.”) And for the reasons explained in the foregoing paragraph, the post-conspiracy period’s use of dummy variables to account for extraordinary circumstances does not render Dr. Raiff s testing methods unreliable. To be sure, Dow is entitled to explain to a jury why it believes the hold-out test is superior to Dr. Raiff s approach. However, Daubert does not hold that a proposed methodology must be the “best” approach in order to be admissible-it instead requires only that the methodology be reliable and based on valid reasoning. See Oddi, 234 F.3d at 145-46; see also In re Urethane Antitrust Litig., No. 04-1616-JWL, 2012 WL 6681783, *6 (D.Kan. Dec. 21, 2012) (failure to utilize particular reliability test “does not provide a basis for exclusion of testimony supported by a [different] test that is admittedly well-accepted in this field.”) Accordingly, the Court rejects Dow’s arguments regarding overfit.
ii. Variable Selection
Dow argues that the models are unreliable because Dr. Raiff did not use objective criteria when selecting variables. Dow Mot. at 28. This argument holds no water because not only did Dr. Raiff select variables based on key economic factors for identifying urethane prices, he also based his selection on the objective AIC process described in Section II.A.Í of this Opinion. Moreover, it should come as no surprise that Dr. Raiff used his expertise to select variables that, in his opinion, impact PPP prices. See, e.g., Resco Products v. Bosai Minerals Grp., 2015 WL 5521768, at *10-11 (admitting expert testimony on pricing regression analysis where expert selected variables based on his knowledge of the relevant industry); In re Urethane Antitrust Litig., 2012 WL 668178, *7 (same); In re Linerboard Antitrust Litig., 497 F.Supp.2d at 671 (same).
Hi. Assigning of Coefficients
Dow also argues that Dr. Raiff s models are unreliable because they use “nonsensical” coefficients that run contrary to basic economic principles. Dow Reply at 5, 50. In one example, Dow explains that Dr. Raiff assigned a negative coefficient to a variable that serves as a proxy for demand. Because basic economic principles hold that prices should increase with demand, Dow contends that the variable in question should have been assigned a positive coefficient. Dow’s argument ignores [508] the fact that the coefficients are not intended to measure variables in isolation. See In re High-Tech Employee Antitrust Litig. (High-Tech Employee), No 11-CV-02509-LHK, 2014 WL 1351040, *21 (N.D.Cal. Apr. 4, 2014) (admitting expert testimony based on regression model where at least one coefficient, in isolation, was counterintuitive). Instead, the variables and their assigned coefficients are designed to measure “the overall impact of all the independent variables on X on the dependent variable Y.” Rev. Raiff Reply at ¶ 92 (emphasis in original).4 As another econometrician explained in a different case involving similar regression models, “[i]t is not appropriate to isolate attention on any one particular variable, it’s like listening only to the oboe player when in fact what you care about is the ensemble.” Daubert Hr’g on Proposed Test, of Halbert White, In re Linerboard (No. MDL 1261), July 2, 2007. See also Rev. Raiff Reply at ¶ 95 (providing illustrative example on how a variable can serve as a predictive proxy for both supply and demand conditions). Consequently, Dow’s objections to Dr. Raiffs coefficient assignments do not warrant exclusion under Daubert.
iv. Statistical Significance
Dow asserts that Dr. Marx’s testimony is inadmissible because the MDI and polyols models do not produce statistically significant results and thus fail to detect any variance between actual and predicted prices. Dow Mot. at 42. When assessing a Daubert challenge, courts may consider the known or potential rate of error. Daubert, 509 U.S. at 594, 113 S.Ct. 2786 (citations omitted). Dow argues that in order for the models to be admissible, there must be less than a 5% chance that the predicted overcharges are merely false positives. See Dow Mot. at 44-48; Dow Reply at 34 n. 24. Taking a step back, when deciding whether results are statistically significant, statisticians will attempt to set an X% significance level at which a null hypothesis can be rejected. The null hypothesis is essentially a strawman which typically posits that a particular relationship demonstrated by a model is due to random chance. If there is less than a 5 in 100 chance that the model’s result occurred randomly, the null hypothesis can be rejected at the 5% significance level. See High-Tech Employee, 2014 WL 1351040 at *8 (citing White v. City of San Diego, 605 F.2d 455, 460 (9th Cir.1979)).
Here, the probability that Dr. Raiffs estimated overcharges are false positives is 19.2% for MDI and 7.2% for polyols. Therefore, the models for those product categories do not meet the 5% statistical significance level that Dow characterizes as a bare minimum requirement for admissibility. While Dow argues that the statistical significance associated with those models should be grounds for exclusion, the Court rejects such an approach as overly rigid. In High-Tech Employee, the district court admitted a regression analysis that failed to meet statistical significance levels that are easily met by the models at issue here. 2014 WL 1351040, at *14 (admitting regression model based on a 50% significance level). In doing so, the court noted that many statisticians caution against overemphasizing statistical significance as a proxy for reliability. See id. at *15 (citing sources). The court also cited to other decisions admitting expert testimony that failed to meet the challenging party’s asserted threshold of statistical significance. See id. (citing Kadas v. MCI [509] Systemhouse Corp., 255 F.3d 359, 362 (7th Cir.2001) and Cook v. Rockwell Int’l Corp., 580 F.Supp.2d 1071, 1082 (D.Colo.2006)). See also, Magistrini v. One Hour Martinizing Dry Cleaning, 180 F.Supp.2d 584, 605 n. 26 (D.N.J.2002) (“Both Drs. Ozonoff and Greenland caution against using statistical significance rigidly, as it is a somewhat arbitrary measure of the import of a particular study, which provides little information about the value of the study.”)
If Dr. Raiffs models were premised on a questionable methodology, the statistical significance issues raised by Dow may be a cause for concern. However, for reasons largely explained already, the Court finds that the models are based on a methodology that is both sound and widely accepted in the econometric community. See High-Tech Employee, 2014 WL 1351040, at *14 (taking overall reliability into account when addressing statistical significance issues). See also DeLuca by DeLuca v. Merrell Dow Pharmaceuticals, Inc., 911 F.2d 941, 955 (3d Cir.1990) (“We stress at the outset that the confidence level or ‘significance’ of a statistical analysis is but a part of a meaningful evaluation of its reliability”) (citing J. Monahan & L. Walker, Social SCIENCE in Law: Cases and Materials 33-75 (1990)). Consequently, the Court concludes that the statistical significance issues go to the weight of the models, not their admissibility. Cf. Sanford Weisberg, Applied Linear Regression 31 (3d ed. 2005) (“Accept-reject rules ... are generally unnecessary for reasonable scientific inquiry. Simply reporting p-values and allowing readers to decide on significance seems a better approach.”)
v. Ability to Account for Differences Among Purchasers and Market Conditions
The Court also rejects Dow’s argument for exclusion on the grounds that the models fail to account for idiosyncrasies amongst different urethane purchasers or the differences in market conditions in general. First, Dr. Raiffs two-step analysis seeks to arrive at “transaction-specific overcharges for the Direct Action Plaintiffs.” Rev. Raiff Reply at ¶ 95. In doing so, the models work to capture the idiosyncrasies and differences among individual Plaintiffs and different urethane products. See id. at ¶ 83. In the context of admissibility under Daubert, the transaction-level approach sufficiently addresses variations that may impact different individual plaintiffs, such as negotiating leverage and bargaining skill. See id. at ¶ 43. Second, the models account for changes in market conditions more generally by using supply and demand variables that account for “booms” and “busts” in the urethanes market. See Marx Dep. 374:5-20, 436:18-438:9. Dow’s argument for exclusion on this basis is therefore unavailing.
vi. Excluding 2001 from the Conspiracy Period
Dow also criticizes Dr. Raiff for excluding 2004 from the conspiracy period. Specifically, Dow notes that despite being initially instructed by Plaintiffs to include 2004 in the conspiracy period, Dr. Raiff excluded that year from the alleged conspiracy in a results-oriented fashion. Dow Reply at 26. After reviewing Plaintiffs’ expert reports, the Court rejects Dow’s position. Dr. Raiffs report sought to determine whether it was “economically appropriate” to include 2004 in the conspiracy period. Rev. Raiff Reply at ¶ 84. In the course of doing so, Dr. Raiff noted, among other things, that (1) average production errors in 2004 were not statistically different from zero; (2) counsel for another urethane supplier represented that the supplier ended price-fixing discussions in 2003; and (3) no urethane supplier has ever claimed that price-fixing had occurred in 2004. See id. at ¶¶ 85-91; see also Marx Rule 26 Disclosure at ¶¶ 61-64. To [510] be clear, the Court does not mean to suggest that Dr. Raiff necessarily made the “right” decision in excluding 2004 from the conspiracy period. See, e.g., In re TMI Litig., 193 F.3d 613, 665 (3d Cir.1999) (“[Plaintiffs do not have to prove then-case twice — they do not have to demonstrate to the judge by a preponderance of the evidence that the assessments of then-experts are correct, they only have to demonstrate by a preponderance of evidence that their opinions are reliable.”) (quotations and citations omitted). The Court merely finds that Dr. Raiffs decision to remove 2004 from the conspiracy is based on valid reasoning and is therefore not a basis for exclusion. See id. (citing Kannankeril v. Terminix Int’l Inc., 128 F.3d 802, 806 (3d Cir.1997)).5
In light of the above, the Court concludes that Plaintiffs’ regression models meet the reliability requirements of Dau-bert.
B. Fit
Dow argues that even assuming the models can reliably detect a variance, Dr. Marx’s opinion must be excluded because she cannot fit her regression analysis to Plaintiffs’ theory of liability. This argument can be split into two conceptual parts. First, Dow argues that Dr. Marx fails to measure the specific impact of the simultaneous price increase announcements that form the basis of Plaintiffs’ claims against Dow. Second, Dow argues that Dr. Marx cannot reliably attribute variance to any particular cause, much less a conspiracy. The Court will address these arguments in turn.
i. Connection to Plaintiffs’ Theory of Liability
Dow finds fault with the models because they fail to specifically account for the conduct that forms the lynchpin of Plaintiffs’ claims against Dow: simultaneous price announcements. In support of their argument, Dow cites Comcast Corp. et al v. Behrend et al., — U.S. —, 133 S.Ct. 1426, 185 L.Ed.2d 515 (2013). In Comcast, the Supreme Court held that the predominance requirements of Rule 23(b)(3) are not met in an antitrust case where the damages model supporting the class claims is not sufficiently tied to each theory of antitrust impact. Extending the rationale of Comcast to this direct action, Dow contends that because Dr. Marx’s models are not specifically tied to price increase announcements, they run afoul of the principle that “[t]he first step in a damages study is the translation of the legal theory of the harmful event into to an analysis of the economic impact of that event.” Comcast, 133 S.Ct. at 1435 (citing Federal Judicial Center, reference Manual on Scientific Evidence 432 (3d ed.2011)).
The Court rejects Dow’s position. As a threshold matter, Comcast is of dubious relevance because it involves predominance issues arising in the Rule 23 context, whereas the instant matter concerns whether expert testimony is reliable and relevant under Rule 702. See High-Tech, 2014 WL 1351040, *17; see also In re Processed Egg Products Antitrust Litig., 81 F.Supp.3d 412, 436 (E.D.Pa.2015) (“Without making any pronouncements about the ability of Direct Purchaser [511] Plaintiffs to clear the Comcast bar, the Court notes simply that, for Daubert purposes at least, the model proposed by Dr. Rausser is rehable and fits the case.”) Indeed, the Comcast Court itself explained that it was not addressing admissibility issues, and instead was focused solely on Rule 23. Comcast, 133 S.Ct. at 1431 n. 4. (noting distinction between admissibility issues under F.R.E. 702 and class certification issues under F.R.C.P. 23)
However, even assuming Comcast does bear on this Court’s Daubert analysis, the exclusion of Dr. Marx’s testimony is not warranted. In Comcast, the plaintiffs accused a cable service provider of entering into unlawful swap agreements in violation of § 1 of the Sherman Act, and monopolizing or attempting to monopolize services in violation of § 2 of the Sherman Act. Id. at 1430. The plaintiffs further alleged that the defendant’s unlawful conduct resulted in four distinct categories of antitrust impact: (1) the withholding of local sports programming from competitors; (2) a reduction in the level of competition from overbuilders; (3) a reduction in the level of benchmark competition; and (4)- an increase in the defendant’s bargaining power relative to content providers. At class certification, the district court held that the plaintiffs could proceed on only one theory of antitrust impact, the deterrence of over-builders. Id. at 1430-31. However, despite having their case trimmed down to one theory, the plaintiffs offered a model that measured damages stemming from all four categories of antitrust impact. The Court held that the class could not be certified under Rule 23(b)(3) because “a model purporting to serve as evidence of damages in [a] class action must measure only those damages attributable to that theory.” Id. at 1433.
The case at bar is distinguishable. Here, Plaintiffs assert a single theory of antitrust liability — that Dow and at least one other entity conspired to fix urethane prices. They also assert a single theory of antitrust impact — that prices for urethanes were artificially inflated. Dr. Marx’s models purport to measure damages stemming from that single category of impact. Unlike the models at issue in Comcast, the models here do not seek to measure damages stemming from an antirust impact that has been rejected or is otherwise no longer at issue. In this Court’s view, Dow’s argument conflates antitrust impact with the specific acts of wrongdoing that form the basis of an alleged antitrust violation. “Impact is a distinct element of liability, independent of proof of a violation and independent of the matter of individual damages.” Glictronix Corp. v. American Tel. and Tel. Co., 603 F.Supp. 552, 587 (D.N.J.1984). Comcast does not require Plaintiffs to tie specific acts of alleged wrongdoing, ie., simultaneous price announcements, to their damages models.6 Assuming the case is applicable here, Comcast merely requires Plaintiffs to link their models to their asserted theory of antitrust impact. Because the models do just that, they are admissible.
ii Attributing Detected Variances to the Conspiracy
As a “fallback position,” Dow argues that even if Dr. Marx is allowed to testify that there was a variance, she should not be permitted to testify as to what caused the variance. Dow’s argument is largely [512] premised on the fact that Dr. Marx has employed a predictive or “reduced form” model that does not specifically attribute the variance to a real world event, and instead simply shows that “something was different” during the conspiracy period. Dow Reply at 15. Therefore, if Dr. Marx can testify regarding variances, it is the jury, not Dr. Marx, who must be tasked with deciding whether to “link” the variances to the alleged conspiracy. Dauberb Hr’g tr. at 189:15-18. In response, Plaintiffs contend that Dr. Marx used a “process of elimination,” which led her to conclude that the variance could have only been caused by a conspiracy. Id. at 11:1— 3.
The Court will reserve on whether Dr. Marx will be able to attribute the variances detected by her models to a conspiracy or the evidence of the conspiracy as a whole. The parties are encouraged to seek a mutually agreeable resolution on whether or how Dr. Marx will testify to that issue.
III. CONCLUSION
For the foregoing reasons, Dow’s motion to exclude the testimony of Dr. Marx is DENIED, subject to the Court reserving on whether Dr. Marx will be permitted to opine that the variances detected in her models are attributable to the alleged conspiracy or are otherwise consistent with the evidence of the conspiracy as a whole.