Individual Causal Inference with Structural Causal Model

TL;DR

This paper introduces a novel approach to individual causal inference using Structural Causal Models, enabling estimation of individual effects by incorporating individual-specific variables into a population-based framework.

Contribution

It proposes the indiv-operator and individual causal query within SCM to facilitate personalized causal effect estimation, bridging population-based models and individual-level inference.

Findings

ICI with SCM enables individualized causal effect estimation.

The indiv-operator formalizes the personalization process.

ICI with SCM focuses on individual alternatives, not counterfactuals.

Abstract

Individual causal inference (ICI) uses causal inference methods to understand and predict the effects of interventions on individuals, considering their specific characteristics / facts. It aims to estimate individual causal effect (ICE), which varies across individuals. Estimating ICE can be challenging due to the limited data available for individuals, and the fact that most causal inference methods are population-based. Structural Causal Model (SCM) is fundamentally population-based. Therefore, causal discovery (structural learning and parameter learning), association queries and intervention queries are all naturally population-based. However, exogenous variables (U) in SCM can encode individual variations and thus provide the mechanism for individualized population per specific individual characteristics / facts. Based on this, we propose ICI with SCM as a "rung 3" causal inference, because it involves "imagining" what would be the causal effect of a hypothetical intervention on an individual, given the individual's observed characteristics / facts. Specifically, we propose the indiv-operator, indiv(W), to formalize/represent the population individualization process, and the individual causal query, P(Y | indiv(W), do(X), Z), to formalize/represent ICI. We show and argue that ICI with SCM is inference on individual alternatives (possible), not individual counterfactuals (non-actual).