Improving Generative Methods for Causal Evaluation via Simulation-Based Inference

• URL: http://arxiv.org/abs/2509.02892v1
• Date: Tue, 02 Sep 2025 23:35:22 GMT
• Title: Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
• Authors: Pracheta Amaranath, Vinitra Muralikrishnan, Amit Sharma, David D. Jensen,
• Abstract summary: Existing generative methods produce synthetic datasets anchored in observed data (source data)<n>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.<n>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.
• Score: 9.22158780078584
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Multi-environment Invariance Learning with Missing Data [0.0]
  In this work, we establish non-asymptotic guarantees on variable selection property and $ell$ error convergence rates.<n>We evaluate the performance of the new estimator through extensive simulations and demonstrate its application using the UCI Bike Sharing dataset.
  arXiv  Detail & Related papers  (2026-01-12T06:30:58Z)
• Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference [89.5628648718851]
  Causal inference is essential for developing and evaluating medical interventions.<n>Real-world medical datasets are often difficult to access due to regulatory barriers.<n>We present STEAM: a novel method for generating Synthetic data for Treatment Effect Analysis in Medicine.
  arXiv  Detail & Related papers  (2025-10-21T16:16:00Z)
• Flow Matching for Robust Simulation-Based Inference under Model Misspecification [11.172752919335394]
  Flow Matching Corrected Posterior Estimation is a framework that refines simulation-trained posterior estimators using a small set of real calibration samples.<n>We show that our proposal consistently mitigates the effects of misspecification, delivering improved inference accuracy and uncertainty calibration compared to standard SBI baselines.
  arXiv  Detail & Related papers  (2025-09-27T16:10:53Z)
• Using Imperfect Synthetic Data in Downstream Inference Tasks [50.40949503799331]
  We introduce a new estimator based on generalized method of moments.<n>We find that interactions between the moment residuals of synthetic data and those of real data can improve estimates of the target parameter.
  arXiv  Detail & Related papers  (2025-08-08T18:32:52Z)
• DUPRE: Data Utility Prediction for Efficient Data Valuation [49.60564885180563]
  Cooperative game theory-based data valuation, such as Data Shapley, requires evaluating the data utility and retraining the ML model for multiple data subsets.<n>Our framework, textttDUPRE, takes an alternative yet complementary approach that reduces the cost per subset evaluation by predicting data utilities instead of evaluating them by model retraining.<n>Specifically, given the evaluated data utilities of some data subsets, textttDUPRE fits a emphGaussian process (GP) regression model to predict the utility of every other data subset.
  arXiv  Detail & Related papers  (2025-02-22T08:53:39Z)
• Assumption-Lean Post-Integrated Inference with Negative Control Outcomes [0.0]
  We introduce a robust post-integrated inference (PII) method that adjusts for latent heterogeneity using negative control outcomes. Our method extends to projected direct effect estimands, accounting for hidden mediators, confounders, and moderators. The proposed doubly robust estimators are consistent and efficient under minimal assumptions and potential misspecification.
  arXiv  Detail & Related papers  (2024-10-07T12:52:38Z)
• Source-Free Domain-Invariant Performance Prediction [68.39031800809553]
  We propose a source-free approach centred on uncertainty-based estimation, using a generative model for calibration in the absence of source data. Our experiments on benchmark object recognition datasets reveal that existing source-based methods fall short with limited source sample availability. Our approach significantly outperforms the current state-of-the-art source-free and source-based methods, affirming its effectiveness in domain-invariant performance estimation.
  arXiv  Detail & Related papers  (2024-08-05T03:18:58Z)
• Estimation of conditional average treatment effects on distributed confidential data [6.798254568821052]
  Conditional average treatment effects (CATEs) can be estimated with high accuracy if data distributed across multiple parties are centralized.<n>It is difficult to aggregate such data owing to confidentiality or privacy concerns.<n>We propose data collaboration double machine learning, a method for estimating CATE models using privacy-preserving fusion data constructed from distributed sources.
  arXiv  Detail & Related papers  (2024-02-05T02:17:21Z)
• Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators [91.3755431537592]
  We show that even in arguably the simplest setting, estimation under ignorability assumptions can be misleading. We consider two popular machine learning benchmark datasets for evaluation of heterogeneous treatment effect estimators. We highlight that the inherent characteristics of the benchmark datasets favor some algorithms over others.
  arXiv  Detail & Related papers  (2021-07-28T13:21:27Z)
• Distributionally Robust Local Non-parametric Conditional Estimation [22.423052432220235]
  We propose a new distributionally robust estimator that generates non-parametric local estimates. We show that despite being generally intractable, the local estimator can be efficiently found via convex optimization. Experiments with synthetic and MNIST datasets show the competitive performance of this new class of estimators.
  arXiv  Detail & Related papers  (2020-10-12T00:11:17Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Decomposed Adversarial Learned Inference [118.27187231452852]
  We propose a novel approach, Decomposed Adversarial Learned Inference (DALI) DALI explicitly matches prior and conditional distributions in both data and code spaces. We validate the effectiveness of DALI on the MNIST, CIFAR-10, and CelebA datasets.
  arXiv  Detail & Related papers  (2020-04-21T20:00:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.