Causal Effects and Overlap in High-dimensional or Sequential Data

Опубликовано 20 ноября 2018, 18:15

Large data sources such as electronic medical records or insurance claims present opportunities to study causal effects of interventions that are difficult to evaluate through experiments. One example is the management of septic patients in the ICU. This typically involves performing several interventions in sequence, the choice of one depending on the outcome of others. Successfully evaluating the effect of these choices depends on strong assumptions, such as having adjusted for all confounding variables. While many argue that having high-dimensional data increases the likelihood of this assumption being true, it also introduces new challenges: the more variables we use for estimating effects, the less likely that patients who received different treatments are similar in all of them. In this talk, we will discuss the role of overlap in causal effect estimation through the lens of domain adaptation and off-policy reinforcement learning.

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