Federal Trade Commission v. Amazon.com, Inc.

Opinion

1 2 3

4 5 UNITED STATES DISTRICT COURT 6 WESTERN DISTRICT OF WASHINGTON AT SEATTLE 7 FEDERAL TRADE COMMISSION, CASE NO. 2:23-cv-00932-JHC 8

ORDER DENYING DEFENDANTS’ 9 Plaintiff, MOTION TO EXCLUDE DR. NEALE MAHONEY’S TESTIMONY 10 v. 11 AMAZON.COM, INC; NEIL LINDSAY, individually and as an officer of 12 Amazon.com, Inc.; RUSSELL GRANDINETTI, individually and as an 13 officer of Amazon.com, Inc.; JAMIL GHANI, individually and as an officer of 14 Amazon.com, Inc.,

15 Defendants. 16

17 I 18 INTRODUCTION 19 This matter comes before the Court on Defendants’ Rule 702 Motion to Exclude Dr. 20 Neale Mahoney’s Testimony. Dkt. # 323. The Court has considered the materials filed in 21 support of and in opposition to the motion, the rest of the file, and the governing law. The Court 22 finds oral argument unnecessary. Being fully advised, for the reasons below, the Court DENIES 23 the motion. 24 1 II BACKGROUND 2 The FTC sued Amazon.com, Inc. and three of the company’s executives, Neil Lindsay, 3 Russell Grandinetti, and Jamil Ghani, claiming that they violated Section 5(a) of the Federal 4 Trade Commission Act (FTC Act), 15 U.S.C. § 45(a), and Section 4 of the Restore Online 5 Shoppers’ Confidence Act (ROSCA), 15 U.S.C. § 8403. Dkt. # 67 at 1–2. The FTC alleges that 6 Defendants tricked, coerced, and manipulated consumers into subscribing to Amazon Prime. Id. 7 at 2. According to the FTC, this was accomplished by failing to disclose the material terms of 8 the subscription clearly and conspicuously and by failing to obtain the consumers’ informed 9 consent before enrolling them. Id. The FTC also alleges that Amazon did not provide simple 10 mechanisms for subscribers to cancel their Prime memberships. Id. at 3. 11 Dr. Neale Mahoney, Ph.D. is a Professor of Economics at Stanford University. Dkt. # 12 370-32 at 8. He received a Ph.D. and M.A. in Economics from Stanford University. Id. And he 13 has taught economics courses at both Stanford University and the University of Chicago’s Booth 14 School of Business. Id. The FTC requested that Mahoney provide an expert opinion on (1) 15 “Whether Amazon’s Cancellation Survey provides a reliable basis from which to draw 16 inferences regarding the behavior of its customers”; (2) “The extent to which customers were 17 unintentionally enrolled in Amazon Prime, and how much such consumers paid to Amazon in 18 Prime membership fees during their memberships”; and (3) “The extent to which customers 19 attempted to cancel their Amazon Prime memberships and believed that they had done so but did 20 not in fact complete the cancellation process, as well as how much such customers subsequently 21 paid to Amazon in Prime membership fees.” Id. at 9. 22 Relevant here, Mahoney’s report offers five opinions: First, Amazon’s Cancellation Survey 23 provides a reliable basis to conclude that a significant number of Prime enrollees unintentionally 24 1 enrolled in Amazon Prime. Id. at 10. Second, unintentional enrollments in Amazon Prime 2 through the at-issue “upsells” led to millions of dollars in harm. Id. at 10–11. Third, a 3 significant number of Prime customers who entered Amazon’s cancellation process did not

4 complete the process and continued to pay Prime subscription fees to Amazon. Id. at 11. 5 Fourth, Prime benefit usage patterns shows that a significant number of Prime subscribers exited 6 the cancellation process with the mistaken belief that they had cancelled their Prime subscription. 7 Id. Fifth, Prime subscribers who exited the cancellation process with the mistaken belief that 8 they had cancelled their Prime subscriptions led to millions of dollars in harm. Id. 9 Defendants move to exclude Mahoney’s expert testimony. Dkt. # 323. They contend 10 that his testimony on unintentional Prime enrollments and cancellations is neither relevant nor 11 reliable. Id. at 8–14. 12 III DISCUSSION 13 A. Legal Standards 14 Federal Rule of Evidence 702 governs the admissibility of expert testimony. Under Rule 15 702, a witness “who is qualified as an expert by knowledge, skill, experience, training, or 16 education may testify in the form of an opinion or otherwise” provided that 17 (a) the expert’s scientific, technical, or other specialized knowledge will help the 18 trier of fact to understand the evidence or to determine a fact in issue;

19 (b) the testimony is based on sufficient facts or data;

20 (c) the testimony is the product of reliable principles and methods; and

21 (d) the expert’s opinion reflects a reliable application of the principles and methods to the facts of the case. 22

Fed. R. Evid. 702. 23 24 1 Courts must ensure “that an expert’s testimony both rests on a reliable foundation and is 2 relevant to the task at hand.” Hyer v. City & Cnty. of Honolulu, 118 F.4th 1044, 1055 (9th Cir. 3 2024) (quoting Elosu v. Middlefork Ranch Inc., 26 F.4th 1017, 1024 (9th Cir. 2022)). They have

4 “broad discretion” in making such evidentiary rulings. Id. (citing City of Pomona v. SQM N. Am. 5 Corp., 866 F.3d 1060, 1065 (9th Cir. 2017)). 6 Expert testimony is relevant if it “will assist the trier of fact to understand the evidence or 7 to determine a fact in issue.” Daubert v. Merrell Dow Pharms., Inc. (“Daubert I”), 509 U.S. 8 579, 589 (1993) (citing Fed. R. Evid. 702(a)). “The relevancy bar [for expert testimony] is low, 9 demanding only that the evidence ‘logically advances a material aspect of the proposing party’s 10 case.’” Messick v. Novartis Pharms. Corp., 747 F.3d 1193, 1196 (9th Cir. 2014) (quoting 11 Daubert v. Merrell Dow Pharm., Inc. (“Daubert II”), 43 F.3d 1311, 1315 (9th Cir. 1995)). 12 “Shaky but admissible evidence is to be attacked by cross examination, contrary evidence, and

13 attention to the burden of proof, not exclusion.” Primiano v. Cook, 598 F.3d 558, 564 (9th Cir. 14 2010). 15 Courts apply four factors in determining whether expert testimony is reliable. These 16 include “1) whether a theory or technique can be tested; 2) whether it has been subjected to peer 17 review and publication; 3) the known or potential error rate of the theory or technique; and 4) 18 whether the theory or technique enjoys general acceptance within the relevant scientific 19 community.” United States v. Hankey, 203 F.3d 1160, 1167 (9th Cir. 2000) (citing Daubert I, 20 509 U.S. at 592–94). But this list of factors is neither exhaustive nor intended to be applied in 21 every case. Id. (citing Kumho Tire Co. v. Carmichael, 526 U.S. 137, 150 (1999)). A court “not 22 only has broad latitude in determining whether an expert’s testimony is reliable, but also in

23 deciding how to determine the testimony’s reliability.” Hangarter v. Provident Life & Accident 24 Ins. Co., 373 F.3d 998, 1017 (9th Cir. 2004) (internal quotations omitted). And “[w]hile 1 evidence that suffer[s] from serious methodological flaws . . . can be excluded, courts are not 2 permitted to determine the veracity of the expert’s conclusions at the admissibility stage.” 3 Teradata Corp. v. SAP SE, 124 F.4th 555, 566 (9th Cir. 2024) (internal quotations and citations

4 omitted) (second alteration in original). 5 The proponent of the expert testimony bears the burden of establishing admissibility by a 6 preponderance of the evidence. See Qualey v. Pierce Cnty., 2025 WL 254810, at *3 (W.D. 7 Wash. Jan. 21, 2025) (citing Daubert I, 509 U.S. at 592 n.10). And courts liberally construe 8 Rule 702 in favor of admissibility. See Daubert I, 509 U.S. at 588; see also Chinn v. Whidbey 9 Pub. Hosp. Dist., 2021 WL 5200171 (W.D. Wash. Nov. 9, 2021). 10 B. Unintentional Enrollment Harm 11 Mahoney’s report details his method to approximate harm from unintentional Amazon 12 Prime enrollments. He started with data from Amazon’s Cancellation Survey, a survey sent to a

13 random selection of Prime subscribers who successfully cancelled their subscription through the 14 online cancellation process. Dkt. # 370-32 at 32. From the survey responses, Mahoney filtered 15 the data for (1) respondentss who enrolled through one of the three at-issue “upsells” the FTC 16 alleges violated ROSCA; and (2) respondents who selected “I did not intend to sign up for 17 Prime” (DNI) when asked, “What was your main reason for cancelling Prime?” Id. at 34, 49. 18 Mahoney thoroughly explains why he finds the data from this survey to be reliable, and he cites 19 pertinent economic literature to support his assertion that “[s]urveys are commonly used by 20 academic economists as a tool for analyzing consumer behavior and decision-making processes.” 21 Id. at 36; see id. at 31–48. 22 From the Cancellation Survey, Mahoney estimates the population of consumers harmed

23 by unintentional Prime enrollments and the resulting subscription fees paid by these customers. 24 Id. at 48. He accomplishes this by using a prediction model based on a linear regression. Id. at 1 49. Mahoney used a broad set of variables for his regression model because his goal was to 2 predict the probability of an unintentional signup for consumers with a wide variety of 3 characteristics. Id. at 49–50. The prediction model was applied to a random sample of Prime

4 subscribers to predict the number of harmed consumers.1 Id. at 51. From the sample group, 5 Mahoney extrapolated the results to the full population of Prime subscribers that he was 6 analyzing to determine the total number of subscribers harmed by unintentional enrollments. Id. 7 In the final step, the damages estimate from the sample was multiplied by the quotient of the full 8 population of Prime subscribers and the sample size to calculate the corresponding harm. Id.; 9 see Dkt. # 343-56 at 176:6–177:3. 10 Mahoney further supports his methodology by describing the steps he took to ensure it 11 was conservative. See Dkt. # 357 at 9. He explains that his use of the Cancellation Survey to 12 estimate unintentional enrollees favored Amazon in two ways. Dkt. # 370-32 at 49. First, the 13 survey does not include unintentional enrollees who did not become aware they were subscribed 14 to Prime during the analysis period or who cancelled their subscription by contacting Amazon 15 customer service. Id. Second, other Amazon cancellation surveys indicate higher rates of 16 unintentional enrollment but could not be linked to the customer data produced by Amazon here. 17 Id. He also rounded all harm estimates down to the nearest million, id. at 51; ran sensitivity 18 checks and used other models that found higher damages, id. at 52–53; and found memory and 19 self-selection bias likely resulted in fewer DNI responses. Id. at 70, 72. 20 Thus, Mahoney’s analysis logically advances the FTC’s argument that consumers were 21 harmed by unintentional enrollment in Amazon Prime. This analysis will similarly help the 22

1 This data comes from Amazon’s “ordinary course data” that includes “customer-level 23 information about signups, cancellations, plans, benefit usage, and Prime transactions. Each dataset includes unique customer identifiers that allow for identifying and linking customers across data sets.” 24 Dkt. # 370-32 at 26. 1 factfinder determine facts that are in dispute. Mahoney also uses techniques that enjoy wide 2 acceptance in the field of economics and he shows that he appropriately applied these techniques 3 to facts of this case. As a result, the Court finds the FTC has met its burden to establish by a

4 preponderance of the evidence that Mahoney’s unintentional enrollment analysis is both relevant 5 and reliable. Fed. R. Evid. 702. 6 Defendants’ arguments to the contrary are unavailing and misapprehend Mahoney’s 7 report.2 Defendants contend that Mahoney’s report is unreliable because it “assumes that the 8 99.998 percent of customers who intended to enroll in Prime nevertheless suffered some 9 compensable harm because a very small fraction of other customers enrolled unintentionally.” 10 See Dkt. # 374 at 6. But this argument is based on the misplaced premise that “[t]he FTC must 11 prove its case by a preponderance of the evidence,” so only the 49 out of 2.7 million customers 12 with a prediction score greater than 50 were likely harmed. Dkt. # 323 at 9. To be sure, the FTC 13 does need to prove its case by a preponderance of the evidence, but that standard is disconnected 14 from the prediction score in Mahoney’s report, which “represents the predicted probability that 15 someone would have responded DNI to [Amazon’s Cancellation] [S]urvey.” Dkt. # 343-56 at 16 191:11–13. The Court is not persuaded that Mahoney should have only considered the harm to 17 consumers with a prediction score greater than 50 because that is the standard of civil liability 18 19

20 2 In their opening brief, Defendants argue that Mahoney’s unintentional enrollment and unintentional cancellation analyses are unreliable because they do not identify any one customer who was 21 harmed. Dkt. # 323 at 10. But Defendants appear to abandon this argument in their Reply, so the Court does not consider it. Compare id. (“Mahoney concedes that he cannot identify any subscriptions that are 22 unintentional enrollments, nor can he distinguish between harm suffered by theoretical unintentional enrollees and other subscribers.”) with Dkt. # 374 at 6 (“Nor does Amazon dispute that damages calculations may permissibly rest on ‘a reasonable approximation to determine the aggregate consumer 23 harm.’”) (quoting Dkt. # 374 at 10). Even so, as discussed below, this argument is contrary to Ninth Circuit precedent. See F.T.C. v. Com. Planet, Inc., 815 F.3d 593, 603 (9th Cir. 2016), abrogated on other 24 grounds by AMG Cap. Mgmt., LLC v. F.T.C., 593 U.S. 67 (2021). 1 the FTC must meet to prove its case. Contrary to Defendants’ arguments, Mahoney provides 2 proof of injury for the consumers that he includes in his analysis. Dkt. # 370-32 at 51. 3 Defendants next argue that “[i]ntent to subscribe is a binary choice; a consumer either

4 meant to sign up or they did not.” Dkt. # 323 at 10. Yet Defendants provide no evidence or 5 caselaw to support this conclusory assertion. Id. The hypotheticals they offer on this point are 6 equally unhelpful because, notably, none involve intent. See Dkt. # 374 at 4. Besides, this 7 argument is a red herring because Mahoney did not set out to conclusively establish which 8 consumers intended to enroll in Prime and which did not. Rather, his report offers a reasonable 9 approximation of the harm caused by unintentional enrollments in Amazon Prime by predicting 10 the total population of unintentional enrollments. Dkt. # 370-32 at 48. This satisfies the FTC’s 11 initial burden in proving the amount it seeks in restitution. Com. Planet, 815 F.3d at 603 (“[T]he 12 FTC bears the burden of proving that the amount it seeks in restitution reasonably approximates

13 the defendant’s unjust gains[.]”). 14 Defendants further criticize Mahoney’s analysis because he assumed “all subscribers who 15 answered [the Amazon Cancellation Survey] by choosing ‘did not intend (DNI)’ are 16 unintentional enrollments.” Dkt. # 323 at 11. This argument defies common logic. It was 17 reasonable for Mahoney to infer the subscribers who selected DNI intended to do so. Even 18 though Defendants have conjured up a hypothetical scenario where a subscriber might have 19 selected DNI when they actually intended to sign up for Prime, that does not make Mahoney’s 20 inference unreasonable. See id.; see also Primiano, 598 F.3d at 565 (“Lack of certainty is not, 21 for a qualified expert, the same thing as guesswork.”). Mahoney used data that was collected 22 from Defendants and by Defendants. Defendants do not show that Mahoney’s analysis fails to

23 distinguish between harmed and unharmed consumers. Contra Dkt. # 323 at 11. 24 1 Defendants’ final argument is that Mahoney failed to consider the benefits that 2 consumers gained from having a Prime subscription. Id. at 4. But this argument contradicts 3 Ninth Circuit precedent. In F.T.C. v. Figgie International, Inc., the court found the defendant

4 needed to provide full refunds to consumers after it misled them about the efficacy of its heat 5 detector products. 994 F.2d 595, 606 (9th Cir. 1993). The defendant claimed its heat detectors 6 were equally effective as smoke detectors, despite knowing this to be false. Id. at 598–600. To 7 explain why it held the customers should be entitled to a full refund, the court analogized the 8 situation to that of a dishonest rhinestone merchant: “Customers who purchased rhinestones sold 9 as diamonds should have the opportunity to get all of their money back. We would not limit 10 their recovery to the difference between what they paid and a fair price for rhinestones.” Id. at 11 606. The court would not limit the customer’s recovery to the difference in price because “[t]he 12 seller’s misrepresentations tainted the customers’ purchasing decisions. If they had been told the

13 truth, perhaps they would not have bought rhinestones at all or only some.” Id. 14 Here, the FTC similarly contends the transactions were tainted by Defendants’ conduct in 15 enrolling users in Prime, not the value of Prime. Dkt. # 67 at 89. So Mahoney complied with 16 Ninth Circuit law when he included the full value of a Prime subscription in his calculation. 17 Even so, Defendants attempt to distinguish Figgie on the basis that it involved deceptive 18 advertising, not “binary choice.” Dkt. # 374 at 7. But, as discussed above, Defendants provide 19 no support for this “binary choice” argument and the Court does not find it persuasive. 20 Defendants also attempt to distinguish Figgie on the basis of a recent Fifth Circuit ruling, but this 21 Court is bound by the Ninth Circuit’s decision in Figgie. Id.; see, e.g., Hart v. Massanari, 266 22 F.3d 1155, 1171 (9th Cir. 2001) (“Circuit law . . . binds all courts within a particular circuit,

23 including the court of appeals itself.”). Thus, the FTC has met its burden of showing that 24 Mahoney’s analysis is reliable even though it does not account for any potential benefits that a 1 consumer gained from having a Prime subscription.3, 4 2 C. Unintentional Cancellation Harm 3 Mahoney’s report also describes his methodology for estimating harm from unsuccessful 4 Amazon Prime cancellations. If a subscriber did not use any Prime benefits after an incomplete 5 cancellation, that was used as an indicator the subscriber intended to cancel but was 6 unsuccessful. Dkt. # 370-32 at 60. Mahoney anticipated that there are other possible 7 explanations for this behavior, such as travel or in-person shopping, so he used Amazon’s data to 8 perform a “difference-in-differences” regression. Id. This regression measured the percentage 9 increase of subscriptions with zero Prime benefit use in the “No Page”5 action group over and 10 above the corresponding increase in subscribers who accepted an offer during the cancellation 11 process (i.e., subscribers who were not considered an unsuccessful cancellation). Id. at 60–61. 12 Mahoney conducted the same analysis for the “Prime Central” action group.6 Id. at 60. The 13 report elaborates that difference-in-differences regressions are a widely accepted statistical 14 15 16 3 Another case identified by Defendants, F.T.C. v. Noland, 2021 WL 5493443 (D. Ariz. Nov. 23, 2021), is similarly nonbinding on the Court. Dkt. # 323 at 9. This case is also distinguishable. Noland 17 focuses on consumer injury that occurred after contract formation. Id. at *3–4; see FTC v. Qyk Brands LLC, et al., 2022 WL 3138761, at *8. Whereas here, like Figgie, the FTC alleges fraud occurred “before 18 obtaining the consumer’s billing information.” Dkt. # 67 at 89; Figgie, 994 F.2d at 606 (“The fraud in the selling, not the value of the thing sold, is what entitles consumers in this case to full refunds”). 19 4 Defendants’ arguments about the inadmissibility of Mahoney’s alternative models are also based on the faulty premise that they “do not identify a single subscriber who was harmed from 20 unintentional enrollment.” Dkt. # 323 at 10. As discussed, this argument is contrary to precedent. See n.2, supra. 21 5 The “No Page” action group is users who did not complete the cancellation process after navigating away from it. Dkt. # 370-32 at 55. For example, subscribers who left the cancellation process 22 and went to a non-Amazon website, closed the browser, or took no action on any page of the cancellation process for at least two hours fall into this group. Id. 6 The “Prime Central” action group consists of the users who did not complete the cancellation 23 process after clicking on a link or feature in the cancellation process that returned them to the Prime Central Page (other than by clicking on the “Keep My Benefits/Keep My Membership” or “Remind Me 24 Later” buttons). Id. at 56. 1 analysis tool “for establishing the impact of an event (in this context, unsuccessful cancellation) 2 on an outcome (Prime benefit usage) and for quantifying the extent of that impact.” Id. at 60–61. 3 The report next details how Mahoney calculated the economic harm suffered by Prime

4 subscribers who exited the cancellation process with the mistaken belief that they cancelled their 5 Prime membership. Id. at 62. The difference-in-differences regression provided an estimate of 6 the share of subscribers in each action group who were harmed by an unsuccessful cancellation. 7 Id. Amazon’s customer data showed the amounts that each Prime subscriber paid in fees during 8 a subscription period. Id. Mahoney used this data to calculate the total payments by subscribers 9 in each action group starting one day after their entry into the cancellation process so harm from 10 unintentional enrollment would not be double counted with harm from unsuccessful cancellation. 11 Id. Total payments by subscribers in the No Page and Prime Central action groups were 12 summed, multiplied by each group’s corresponding difference-in-differences regression

13 coefficient, and extrapolated to the full population of Prime subscribers. Id. This provided the 14 estimated dollar harm for each group.7 15 Mahoney also explained how he controlled for other factors that made his estimate 16 conservative. See Dkt. # 357 at 13. Among other things, he controlled for factors that could 17 affect Prime benefit usage. Dkt. # 370-32 at 60. He did not include Prime subscribers who 18 wanted to cancel but could not find the End Membership option to start the cancellation process. 19 Id. at 54. Subscribers who tried to cancel and failed through other means, for instance by 20 cellphone, were not included. Id. Mahoney also excluded harm to consumers who entered the 21 cancellation process a second time. Id. at 62. 22

23 7 Mahoney also showed that he used similar methods to perform additional calculations on behalf of the FTC. See Dkt. # 370-32 at 63–64. But Defendants do not challenge the admissibility of these 24 calculations. Dkt. # 323 at 12–14. 1 Much like his analysis of harm from unintentional enrollments, Mahoney’s unintentional 2 cancellation analysis logically advances the FTC’s argument that consumers were harmed when 3 they mistakenly believed they cancelled their Amazon Prime subscription. This analysis will

4 also help the factfinder determine facts that are in dispute. Mahoney uses techniques that enjoy 5 wide acceptance in the field of economics and he shows that he appropriately applied these 6 technique to the facts of this case. As a result, the Court finds the FTC met its burden to 7 establish by a preponderance of the evidence that Mahoney’s unintentional cancellation analysis 8 is both relevant and reliable. Fed. R. Evid. 702. 9 Defendants contend that this analysis is flawed too. They fault Mahoney for failing to 10 failing to “separate lawful from unlawful conduct.” Dkt. # 323 at 13. But the FTC alleges “all 11 Prime subscriptions involve unlawful conduct as Amazon never had ROSCA-compliant 12 cancellation.” Dkt. # 357 at 13–14; see Dkt. # 67 at 90. And, again, the hypothetical scenarios

13 envisioned by Defendants do not render Mahoney’s analysis unreliable, nor do they show he 14 cannot separate lawful from unlawful conduct. See Dkt. # 323 at 11; Section III.B, supra. 15 Mahoney approximated harm to consumers and made reasonable assumptions in doing so. See 16 Com. Planet, 815 F.3d at 603; Dkt. # 357 at 14. These purported issues are “best settled by a 17 battle of the experts before the fact finder, not by judicial fiat.” City of Pomona, 750 F.3d at 18 1049.8 19

20 8 Defendants also make the argument that Mahoney’s unintentional cancellation analysis is “far outside of the standard methodology used in the relevant field,” and is therefore unreliable, because he 21 “failed to employ a standard ‘parallel trends’ analysis[.]” Dkt. # 323 at 10. For one thing, this argument is made in a footnote and undeveloped, so it is waived. See John-Charles v. California, 646 F.3d 1243, 22 1247 (9th Cir. 2011). But it is also disingenuous. Defendants did not conduct a parallel trends analysis when conducting a similar “Difference-in-Difference analysis to estimate the impact of unintentional Prime signups[.]” Dkt. # 359-21 (sealed). And as Mahoney explained at his deposition, this analysis is 23 simply “one approach to provide support for the methodology.” Dkt. # 343-56 at 237:6–8. Defendants similarly omit that Mahoney identified academic work in which he did not use a parallel trends analysis 24 when conducting a difference-in-differences regression. Dkt. # 343-56 at 235:21–236:16. l Defendants’ final argument is that Mahoney’s unintentional cancellation analysis is 2 || unreliable because it fails to consider the benefits Prime subscribers received when they failed to 3 cancel their memberships. Dkt. # 323 at 13. This argument fails for the same reasons it failed 4 for Mahoney’s unintentional enrollment analysis. See Section III.B, supra. The FTC alleges the 5 fraud was in Prime’s cancellation mechanisms, not the value of Prime. Dkt. # 67 at 90. Thus, it 6 || was appropriate for Mahoney to include the full value of subscribers’ Prime subscription in his 7 analysis. 8 IV 9 CONCLUSION

10 For the reasons above, the Court DENIES the motion. Dkt. #323.

Dated this 22st day of August, 2025.

12 gh 4 Chur United States District Judge 14 15 16 17 18 19 20 21 22 23 24 ORDER DENYING DEFENDANTS’ MOTION TO EXCLUDE DR. NEALE MAHONEY’S