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Calibrated Propensities for Causal Effect Estimation
Shachi Deshpande · Volodymyr Kuleshov
Event URL: https://openreview.net/forum?id=enWssuqNhJ »

Propensity scores are commonly used to balance observed confounders while estimating treatment effects. When the confounders are high-dimensional or unstructured, the learned propensity scores can be miscalibrated and ineffective in the correction of confounding. We argue that the probabilistic output of a learned propensity score model should be calibrated, i.e. predictive treatment probability of 90% should correspond to 90% individuals being assigned the treatment group. We investigate the theoretical properties of a calibrated propensity score model and its role in unbiased treatment effect estimation. We demonstrate improved causal effect estimation with calibrated propensity scores in several tasks including high-dimensional genome-wide association studies.

Author Information

Shachi Deshpande (Cornell University)
Volodymyr Kuleshov (Cornell Tech)

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