Deep End-to-end Causal Inference

Published: 17 Jun 2024, Last Modified: 17 Jun 2024Accepted by TMLREveryoneRevisionsBibTeX
Abstract: Causal inference is essential for data-driven decision-making across domains such as business engagement, medical treatment, and policy making. However, in practice, causal inference suffers from many limitations including unknown causal graphs, missing data problems, and mixed data types. To tackle those challenges, we develop Deep End-to-end Causal Inference (DECI) framework, a flow based non-linear additive noise model combined with variational inference, which can perform both Bayesian causal discovery and inference. Theoretically, we show that DECI unifies many existing structural equation model (SEM) based causal inference techniques and can recover the ground truth mechanism under standard assumptions. Motivated by the challenges in the real world, we further extend DECI to heterogeneous, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Empirically, we conduct extensive experiments (over a thousand) to show the competitive performance of DECI when compared to relevant baselines for both causal discovery and inference with both synthetic and causal machine learning benchmarks across data types and levels of missingness.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=6Rn3nGyvcD&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DTMLR%2FAuthors%23your-submissions)
Changes Since Last Submission: No major changes compared to the previous rebuttal version, since our paper is rejected due to uploading the de-anonymized revision. The difference compared to the initial submission can be seen in the author's response in the previous TMLR submission Url.
Code: https://github.com/microsoft/causica/tree/v0.0.0
Assigned Action Editor: ~Fredrik_Daniel_Johansson1
Submission Number: 2271
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