ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on conditional average treatment effect is equivalent to minimizing an integrated posterior variance, similar to Cohn criteria \citep{cohn1994active}. We theoretically prove ABC3 also minimizes an imbalance between the treatment and control groups and the type 1 error probability. Imbalance-minimizing characteristic is especially notable as several works have emphasized the importance of achieving balance. Through extensive experiments on real-world data sets, ABC3 achieves the highest efficiency, while empirically showing the theoretical results hold.
Code (1)
Tasks
Active LearningCausal InferenceExperimental DesignSimilar Papers 제목 키워드 기반
Foundation Models for Causal Inference via Prior-Data Fitted Networks
Prior-data fitted networks (PFNs) have recently been proposed as a promising way to train tabular foundation models. PFNs are transformers that are pre-trained on synthetic data generated from a prespecified prior distri…
Bayesian InferenceCausal InferenceIn-Context LearningActive Bayesian Causal Inference
Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage …
Active LearningCausal DiscoveryCausal InferenceGaussian ProcessesExplaining data using causal Bayesian networks
I introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Networks and suggest and illustrate a research a…
Amortized Bayesian Causal Discovery of Extended Factor Graphs
Learning causal graphs from interventional data is a challenging problem with broad applications. In molecular biology, for example, a central goal is to uncover gene regulatory networks from large-scale perturbation dat…
Bayesian InferenceActive Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal
Science and Engineering applications are typically associated with expensive optimization problems to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning comp…
Active LearningBayesian OptimizationGeneral Classification