# Estimating Individual Treatment Effects through Causal Populations Identification

arXiv: https://arxiv.org/abs/2004.05013 · HTML: https://mlsanity.com/paper/2004.05013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Non parametric estimation of causal populations in a counterfactual scenario](https://mlsanity.com/paper/2112.04288/) (2021)

## Abstract (arXiv)

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.