Online Causal Inference Seminar

Free and open to the public
All seminars are on Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin).

• Tuesday, August 3, 2021 [Link to join] (ID: 995 8569 5110, Password: 007080)
  - Speakers: Anish Agarwal (MIT) and Dennis Shen (Berkeley)
  - Title: Synthetic Inverventions
  - Discussant: Jason Poulos (Harvard)

- Abstract: Consider a setting where there are N heterogeneous units (e.g., individuals, sub-populations) and D interventions (e.g., socio-economic policies). Our goal is to learn the potential outcome associated with every intervention on every unit (i.e., N x D causal parameters). Towards this, we present a causal framework, synthetic interventions (SI), to infer these N x D causal parameters while only observing each of the N units under at most two interventions, independent of D. This can be significant as the number of interventions, i.e, level of personalization, grows. Importantly, our estimator also allows for latent confounders that determine how interventions are assigned. Theoretically, under a novel tensor factor model across units, measurements, and interventions, we formally establish an identification result for each of these N x D causal parameters, and establish finite-sample consistency and asymptotic normality of our estimator. Empirically, we validate our framework through both experimental and observational case studies; namely, a large-scale A/B test performed on an e-commerce platform, a phase 3 clinical trial data from a pharmaceutical company, and an evaluation of mobility-restricting policies on COVID-19. We believe this has important implications for program evaluation and the design of data-efficient RCTs with heterogeneous units and multiple interventions.
[Paper] [Slides]