Biostatistics Special Seminar

Abstract: "In many social science applications, researchers use the difference-in-differences (DID) estimator to establish causal relationships, exploiting cross-sectional variation in a baseline factor and temporal variation in exposure to an event that presumably may affect all units. This approach, which we term factorial DID (FDID), differs from canonical DID in that it lacks a clean control group unexposed to the event after the event occurs. In this paper, we clarify FDID as a research design in terms of its data structure, feasible estimands, and identifying assumptions that allow the DID estimator to recover these estimands. We frame FDID as a factorial design with two factors: the baseline factor, denoted by G, and the exposure level to the event, denoted by Z, and define the effect modification and causal interaction as the associative and causal effects of G on the effect of Z, respectively. We show that under the canonical no anticipation and parallel trends assumptions, the DID estimator identifies only the effect modification of G in FDID, and propose an additional factorial parallel trends assumption to identify the causal interaction. Moreover, we show that the canonical DID research design can be reframed as a special case of the FDID research design with an additional exclusion restriction assumption, thereby reconciling the two approaches. We extend this framework to allow conditionally valid parallel trends assumptions and multiple time periods, and clarify assumptions required to justify regression analysis under FDID. We illustrate these findings with empirical examples from economics and political science, and provide recommendations for improving practice and interpretation under FDID."

About the speaker: Dr. Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley, specializing in causal inference and observational studies. He received his Ph.D. from the Department of Statistics at Harvard University in May 2015, under the guidance of Dr. Donald Rubin, and later completed postdoctoral research in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health. His work has earned him several prestigious awards, including the NSF CAREER Award, the COPSS Emerging Leader Award, and the Royal Statistical Society's Guy Medal in Bronze. Dr. Ding is also the author of the recent influential textbook A First Course in Causal Inference. 

Join Zoom Meeting
https://nyu.zoom.us/j/99021674864

Meeting ID: 990 2167 4864

---

One tap mobile
+16465588656,,99021674864# US (New York)
+13017158592,,99021674864# US (Washington DC)

---

Dial by your location
• +1 646 558 8656 US (New York)
• +1 301 715 8592 US (Washington DC)
• +1 312 626 6799 US (Chicago)
• +1 346 248 7799 US (Houston)
• +1 669 900 6833 US (San Jose)
• +1 253 215 8782 US (Tacoma)

Meeting ID: 990 2167 4864

Find your local number: https://nyu.zoom.us/u/ad4caklVLX

---

Join by SIP
• 99021674864@zoomcrc.com

---

Join by H.323
• 162.255.37.11 (US West)
• 162.255.36.11 (US East)
• 221.122.88.195 (Mainland China)
• 115.114.131.7 (India Mumbai)
• 115.114.115.7 (India Hyderabad)
• 213.19.144.110 (Amsterdam Netherlands)
• 213.244.140.110 (Germany)
• 103.122.166.55 (Australia Sydney)
• 103.122.167.55 (Australia Melbourne)
• 209.9.211.110 (Hong Kong SAR)
• 149.137.40.110 (Singapore)
• 64.211.144.160 (Brazil)
• 159.124.132.243 (Mexico)
• 159.124.168.213 (Canada Toronto)
• 65.39.152.160 (Canada Vancouver)
• 207.226.132.110 (Japan Tokyo)
• 149.137.24.110 (Japan Osaka)

Meeting ID: 990 2167 4864