Causal Inference Reading Group (Fall 2016)



Meeting time: Wednesdays 1-3pm, Evans 1011.

Mailing list: (subscribe here)

Tentative reading list for this semester

Google spreadsheet for collaborative scheduling

Reading group tips for presenters and listeners (courtesy Lester Mackey, Percy Liang, and their reading groups)

Theme: Modern causal inference in complex models

Many recent breakthroughs in research and industry lie at the intersection of machine learning, econometrics, and causal inference. We will discuss and compare recent literature in two main topic areas:

  • High dimensional causal inference. Traditional methods in casual inference, like the propensity score, can break down in high dimensional settings. Machine learning methods can also improve otherwise-standard approaches, such as covariate adjustment in randomized trials.

  • Causal inference with interference. Standard causal inference methods typically assume that units are independent of each other. This assumption, however, often breaks down in practice, such as in large-scale social networks (e.g., Facebook) and or vaccine trials.

Both of these are highly active research areas, with many challenging and unresolved questions that bear on important practical problems. At each meeting a group member will present one paper, followed by a discussion of the topic and how it relates to the other topics we have covered.