What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms

2026-10-06

AI Summary

This study addresses the challenge of efficiently selecting observational variables for causal effect identification in partially specified causal models. Focusing on iterative identification within semi-Markovian models, this work leverages causal graph theory to establish necessary and sufficient graphical conditions under which previously unidentifiable queries become identifiable. Furthermore, it designs an efficient localization algorithm to optimize navigation through the model space and streamline the search for optimal observation strategies. By refining underlying identification mechanisms to precisely pinpoint critical observation opportunities, this research significantly enhances both the efficiency and accuracy of constructing identification strategies in causal inference.

Abstract

Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which can then be used to deduce its logical implications. Yet, in practical applications, model specification is often an iterative process, and involves exploration and introspection: of the many aspects of the phenomenon one could investigate, which ones actually matter for the identification of the causal effect of interest? In this paper we study the problem of iterative identification in partially specified causal models. We focus on determining where observing variables that intercept a direct effect or a confounding path between two variables could enable identification in semi-Markovian models. We give necessary and sufficient graphical conditions for when observing such variables can render an unidentifiable query identifiable, together with an efficient algorithm for locating all such opportunities in a given causal diagram. Our results can help analysts better navigate the model space by drawing attention to the parts of their substantive knowledge that could result in a successful identification strategy.

Problem

• causal inference
• iterative identification
• partially specified causal models
• semi-Markovian models
• model specification

Innovation

• Causal Inference
• Iterative Identification
• Semi-Markovian Models
• Graphical Conditions
• Algorithm