Characterization and Learning of Causal Graphs from Hard Interventions

TL;DR

This paper develops a framework for characterizing and learning causal graphs from data obtained through hard interventions, extending causal discovery methods to incorporate interventional data and do-calculus.

Contribution

It introduces graphical constraints linked to do-calculus for hard interventions, characterizes interventional equivalence classes, and proposes a new learning algorithm with orientation rules.

Findings

Proposes graphical constraints for hard interventions.

Characterizes interventional equivalence classes with latent variables.

Introduces a sound learning algorithm with new orientation rules.

Abstract

A fundamental challenge in the empirical sciences involves uncovering causal structure through observation and experimentation. Causal discovery entails linking the conditional independence (CI) invariances in observational data to their corresponding graphical constraints via d-separation. In this paper, we consider a general setting where we have access to data from multiple experimental distributions resulting from hard interventions, as well as potentially from an observational distribution. By comparing different interventional distributions, we propose a set of graphical constraints that are fundamentally linked to Pearl's do-calculus within the framework of hard interventions. These graphical constraints associate each graphical structure with a set of interventional distributions that are consistent with the rules of do-calculus. We characterize the interventional equivalence class of causal graphs with latent variables and introduce a graphical representation that can be used to determine whether two causal graphs are interventionally equivalent, i.e., whether they are associated with the same family of hard interventional distributions, where the elements of the family are indistinguishable using the invariances from do-calculus. We also propose a learning algorithm to integrate multiple datasets from hard interventions, introducing new orientation rules. The learning objective is a tuple of augmented graphs which entails a set of causal graphs. We also prove the soundness of the proposed algorithm.