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Abstract:
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Perhaps the terms "causal inference" and "machine learning" mix like oil and water. Machine learning models are often black box complicated functions that provide predictions without causal explanations. For causal inference, this kind of model is often unacceptable. Maybe we can find ways to harness the predictive power of machine learning methods for the purpose of causal inference. In particular, I will discuss some of the new techniques we are developing for predicting conditional differences. This involves Bayesian models for batch data and for longitudinal data, and also matching methods.
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