BibTeX

@article{2509.02892v1,
Author        = {Pracheta Amaranath and Vinitra Muralikrishnan and Amit Sharma and David D. Jensen},
Title         = {Improving Generative Methods for Causal Evaluation via Simulation-Based
  Inference},
Eprint        = {2509.02892v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Generating synthetic datasets that accurately reflect real-world
observational data is critical for evaluating causal estimators, but 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, existing methods typically require users to provide
point estimates of such parameters (rather than distributions) and fixed
estimates (rather than estimates that can be improved with reference to the
source data). This denies users the ability to express uncertainty over
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 models generative
parameters as uncertain and infers their posterior distribution given a source
dataset. Leveraging techniques in simulation-based inference, SBICE identifies
parameter configurations that produce synthetic datasets closely aligned with
the source data distribution. Empirical results demonstrate that SBICE improves
the reliability of estimator evaluations by generating more realistic datasets,
which supports a robust and data-consistent approach to causal benchmarking
under uncertainty.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.02892v1},
File          = {2509.02892v1.pdf}
}