Estimating Individual Treatment Effects through Causal Populations Identification
This work addresses a challenging problem in causal learning for researchers and practitioners, but it appears incremental as it builds on existing EM algorithm frameworks.
The paper tackles the problem of estimating individual treatment effects from observational data by formulating it as an inference from hidden variables and enforcing causal constraints based on a model of four exclusive causal populations, proposing the Expected-Causality-Maximization (ECM) algorithm and comparing it to baseline methods on synthetic and real-world data.
Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one of both, is a challenging problems in causal learning. In this paper, we formulate this problem as an inference from hidden variables and enforce causal constraints based on a model of four exclusive causal populations. We propose a new version of the EM algorithm, coined as Expected-Causality-Maximization (ECM) algorithm and provide hints on its convergence under mild conditions. We compare our algorithm to baseline methods on synthetic and real-world data and discuss its performances.