Targeted Sequential Indirect Experiment Design

2024-05-30

Neural Information Processing Systems

AI Summary

This paper addresses the challenge of estimating causal effects when the target variable cannot be directly intervened upon and the underlying mechanism is complex—nonlinear, high-dimensional, and confounded. We propose the first active experimental design framework tailored for *indirect experiments*. Methodologically, we formulate a bilevel optimization model that integrates kernel-based estimation with adaptive sequential experimental design, yielding an analytically tractable and computationally efficient estimator for upper and lower bounds on the causal effect. Our key contributions are: (1) the first systematic formalization of feasibility conditions for indirect intervention under nonlinear confounding; and (2) dynamic narrowing of the causal bound gap to precisely localize the target query value. Extensive synthetic experiments across diverse settings demonstrate that our method significantly improves causal effect identification accuracy, with faster convergence of bound width compared to state-of-the-art baselines.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Scientific hypotheses typically concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms, such as the effect of gene expression levels on phenotypes or how microbial communities influence environmental health. Such queries are inherently causal (rather than purely associational), but in many settings, experiments can not be conducted directly on the target variables of interest, but are indirect. Therefore, they perturb the target variable, but do not remove potential confounding factors. If, additionally, the resulting experimental measurements are multi-dimensional and the studied mechanisms nonlinear, the query of interest is generally not identified. We develop an adaptive strategy to design indirect experiments that optimally inform a targeted query about the ground truth mechanism in terms of sequentially narrowing the gap between an upper and lower bound on the query. While the general formulation consists of a bi-level optimization procedure, we derive an efficiently estimable analytical kernel-based estimator of the bounds for the causal effect, a query of key interest, and demonstrate the efficacy of our approach in confounded, multivariate, nonlinear synthetic settings.

Problem

• Designs indirect experiments to study causal mechanisms.
• Addresses confounding factors in multivariate, nonlinear systems.
• Develops adaptive strategy to narrow query bounds efficiently.

Innovation

• Adaptive strategy for indirect experiment design
• Sequential narrowing of causal effect bounds
• Analytical kernel-based estimator for efficiency