Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources

TL;DR

This paper introduces a method to approximate bounds for counterfactual queries by fusing observational and interventional data, even if biased, using a causal EM scheme and graphical transformations.

Contribution

It extends counterfactual bounds approximation to multiple biased and unbiased datasets through a novel EM approach and graphical data remapping.

Findings

The likelihood function has no local maxima, enabling stable EM optimization.

The method effectively fuses heterogeneous data sources for informative counterfactual bounds.

Numerical experiments and a case study demonstrate the approach's practical effectiveness.

Abstract

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data has no local maxima. This enables us to use the causal expectation-maximisation scheme to approximate the bounds for partially identifiable counterfactual queries, which are the focus of this paper. We then show how the same approach can address the general case of multiple datasets, no matter whether interventional or observational, biased or unbiased, by remapping it into the former one via graphical transformations. Systematic numerical experiments and a case study on palliative care show the effectiveness of our approach, while hinting at the benefits of fusing heterogeneous data sources to get informative outcomes in case of partial identifiability.