Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"

2025-10-16

AI Summary

This paper addresses the causal interpretation of missing data mechanisms, tackling longstanding theoretical debates—particularly concerning the “ignorability” assumption. Building upon the potential outcomes framework and causal graphical models, it establishes a unified counterfactual modeling paradigm for missingness. Through formal proofs and conceptual clarification, the work precisely characterizes the conditions under which missingness mechanisms are causally identifiable and corrects widespread misinterpretations of assumptions such as MAR (Missing at Random). It systematically engages critiques from multiple scholars, rigorously distinguishing statistical ignorability from causal ignorability, and proposes a refined, causally grounded taxonomy of missingness mechanisms. The results strengthen the theoretical foundations of causal inference with missing data and enhance its applicability and interpretability in statistical modeling and machine learning. (138 words)

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We are grateful to the discussants, Levis and Kennedy [2025], Luo and Geng [2025], Wang and van der Laan [2025], and Yang and Kim [2025], for their thoughtful comments on our paper (Nabi et al., 2025). In this rejoinder, we summarize our main contributions and respond to each discussion in turn.

Problem

• Addressing causal and counterfactual perspectives on missing data
• Responding to methodological discussions about missing data models
• Clarifying main contributions regarding missing data causal frameworks

Innovation

• Addressing missing data with causal models
• Using counterfactual frameworks for analysis
• Responding to peer discussions on methods