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Machine learning & nonparametric efficiency in causal inference

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CIFW01 - Foundations of causal inference

This short course covers the basics of efficient nonparametric estimation in causal inference, including estimating equations, TMLE , and double machine learning. It considers nonparametric efficiency bounds for causal estimands, and efficient bias-corrected estimators based on influence functions. Importantly, these estimators yield fast rates of convergence and normal limiting distributions, even in complex nonparametric models where nuisance functions (e.g., propensity scores) are estimated with modern machine learning tools. The estimators are often doubly robust. Background in mathematical statistics is useful but not required. The workshop covers both theory and application, including R code for implementing various methods.

This talk is part of the Isaac Newton Institute Seminar Series series.

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