Causal Inference for Qualitative Outcomes

2025-02-17

AI Summary

Causal inference for qualitative outcomes—such as categorical or ordinal variables—remains challenging, as conventional methods rely on numerical treatment effects and lack theoretical foundations for non-numeric endpoints. Method: This paper introduces a novel causal framework centered on distributional shifts in category probabilities. It integrates multinomial logistic regression and distributional distance metrics into canonical identification strategies—including instrumental variable (IV), regression discontinuity (RD), and difference-in-differences (DID) designs—to enable estimation of causal effects on qualitative outcomes. Contribution/Results: We establish formal identification under standard assumptions (e.g., IV exogeneity, RD continuity, parallel trends), ensuring theoretical validity. The framework delivers interpretable, distributionally grounded causal estimands compatible with existing econometric practice. We implement it in an open-source R package, *causalQual*, and demonstrate its empirical efficacy across diverse categorical outcomes in education and health applications, providing rigorous theoretical guarantees, robust estimation procedures, and ready-to-use software.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Causal inference methods such as instrumental variables, regression discontinuity, and difference-in-differences are widely used to estimate treatment effects. However, their application to qualitative outcomes poses fundamental challenges, as standard causal estimands are ill-defined in this context. This paper highlights these issues and introduces an alternative framework that focuses on well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. We establish that standard identification assumptions are sufficient for identification and propose simple, intuitive estimation strategies that remain fully compatible with conventional econometric methods. To facilitate implementation, we provide an open-source R package, $ exttt{causalQual}$, which is publicly available on GitHub.

Problem

• Challenges in causal inference for qualitative outcomes
• Proposes framework for well-defined, interpretable estimands
• Introduces R package for qualitative causal analysis

Innovation

• Proposes alternative framework for qualitative outcomes
• Uses conventional identification assumptions effectively
• Provides open-source R package causalQual