[2509.02892] Improving Generative Methods for Causal Evaluation via Simulation-Based Inference

Computer Science > Machine Learning arXiv:2509.02892 (cs) [Submitted on 2 Sep 2025 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Improving Generative Methods for Causal Evaluation via Simulation-Based Inference Authors:Pracheta Amaranath, Vinitra Muralikrishnan, Amit Sharma, David Jensen View a PDF of the paper titled Improving Generative Methods for Causal Evaluation via Simulation-Based Inference, by Pracheta Amaranath and 2 other authors View PDF HTML (experimental) Abstract:Generating synthetic datasets that accurately reflect real-world observational data is critical for evaluating causal estimators, but it remains a challenging task. Existing generative methods offer a solution by producing synthetic datasets anchored in the observed data (source data) while allowing variation in key parameters such as the treatment effect and amount of confounding bias. However, it is often unclear which generative methods to use and which values of parameters to choose when generating synthetic datasets. Moreover, existing methods typically require users to provide fixed point estimates of such parameters. This denies users the ability to express uncertainty over both generative methods and parameter values and removes the potential for posterior inference, potentially leading to unreliable estimator comparisons. We introduce simulation-based inference for causal evaluation (SBICE), a framework that treats the generative method and its corresponding generative parameters ...