Obtaining Causal Information by Merging Datasets with MAXENT

TL;DR

This paper explores how to infer causal relationships and quantify causal effects by merging datasets with partial observations using the maximum entropy principle, addressing challenges in causal inference with incomplete data.

Contribution

It introduces a novel approach leveraging maximum entropy to identify causal edges from datasets with different observed variables, assuming causal sufficiency and faithfulness.

Findings

Maximum entropy can identify causal edges with partial data

Method works under assumptions of causal sufficiency and faithfulness

Enables causal inference without joint observation of all variables

Abstract

The investigation of the question "which treatment has a causal effect on a target variable?" is of particular relevance in a large number of scientific disciplines. This challenging task becomes even more difficult if not all treatment variables were or even cannot be observed jointly with the target variable. Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders. In this paper, we discuss how causal knowledge can be obtained without having observed all variables jointly, but by merging the statistical information from different datasets. We show how the maximum entropy principle can be used to identify edges among random variables when assuming causal sufficiency and an extended version of faithfulness, and when only subsets of the variables have been observed jointly.