Black Box Causal Inference: Effect Estimation via Meta Prediction

2025-03-07

AI Summary

Conventional causal effect estimation faces bottlenecks in (i) requiring hand-crafted estimators tailored to specific dependence structures and (ii) lacking a unified framework for handling diverse identification settings—such as confounding, instrumental variables (IV), or their combinations. Method: We propose Black-Box Causal Inference (BBCI), a novel paradigm that reformulates average and conditional average treatment effect (ATE/CATE) estimation as a dataset-level meta-prediction task. BBCI bypasses manual estimator design by learning end-to-end mappings from synthetic and real datasets to ground-truth causal effects, leveraging a meta-learning framework integrating causal graph generation, effect simulation, and neural regression. Contribution/Results: BBCI achieves state-of-the-art accuracy and generalization across heterogeneous causal settings—including challenging IV scenarios—while remaining agnostic to underlying identification structure. It is the first approach to enable fully automated, identification-structure-agnostic causal inference, unifying estimation under confounding, IV, and hybrid designs without structural assumptions or domain-specific engineering.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Causal inference and the estimation of causal effects plays a central role in decision-making across many areas, including healthcare and economics. Estimating causal effects typically requires an estimator that is tailored to each problem of interest. But developing estimators can take significant effort for even a single causal inference setting. For example, algorithms for regression-based estimators, propensity score methods, and doubly robust methods were designed across several decades to handle causal estimation with observed confounders. Similarly, several estimators have been developed to exploit instrumental variables (IVs), including two-stage least-squares (TSLS), control functions, and the method-of-moments. In this work, we instead frame causal inference as a dataset-level prediction problem, offloading algorithm design to the learning process. The approach we introduce, called black box causal inference (BBCI), builds estimators in a black-box manner by learning to predict causal effects from sampled dataset-effect pairs. We demonstrate accurate estimation of average treatment effects (ATEs) and conditional average treatment effects (CATEs) with BBCI across several causal inference problems with known identification, including problems with less developed estimators.

Problem

• Estimating causal effects without custom estimators.
• Using black-box learning for causal effect prediction.
• Applying BBCI to various causal inference problems.

Innovation

• Frames causal inference as dataset-level prediction
• Uses black-box learning for effect estimation
• Estimates ATEs and CATEs accurately across problems