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NeurIPS 2026Cornell Cornell Tech, Cornell Causal inference

Never Go Full Batch: Stochastic TMLE for Large-Scale Debiased Inference

Diyang Li, Fei Wang, Kyra Gan

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Massachusetts Institute of Techn MIT Causal inference

Causal Inference for Sequential Settings under Interference and Latent Confounding

Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U New South Wales U California, San Diego UCSD Samsung Research America Causal inference

PACO: Partial-order-Augmented Continuous Optimization for Differentiable Causal Discovery

Xiaoxuan Li, Junda Wu, Julian McAuley, Lina Yao and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Sydney Mohamed bin Zayed University of Causal inference

Causal Effect Identification with a Single Agnostic Proxy

Xiu-Chuan Li, Tongliang Liu

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026UCL, Gatsby Google Deepmind / UCL Causal inference

Semiparametric Efficient Tests for Interpretable Distributional Treatment Effects

Houssam Zenati, Arthur Gretton

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of Adelaide The University of Melbourne The Hong Kong University of Scie Peking Unimelb & MBZUAI Causal inference

Bayesian Causal Experimental Design for CATE Estimation under Noncompliance

Erdun Gao, Yuanyuan Wang, Liang Zhang, Yuhang Liu and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Shanghai University of Finance a shanghai IIIS Causal inference

High-Dimensional Conditional Independence Testing via Random Projection Aggregation

Dian Jin, zirui chen, Ting Li, Jiaye Teng

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026RIKEN Keio Causal inference

Bayesian Causal Stress Testing: Posterior Fragility of Treatment-Effect Conclusions

Makoto Nakakita, Teruo Nakatsuma

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Chicago CMU, Carnegie Mellon Chicago Booth School of Business Causal inference

ML-assisted Randomization Tests for A/B Experiments

Wenxuan Guo, JungHo Lee, Panos Toulis

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026SpotlightColumbia RWTH Aachen Mohamed bin Zayed University of Causal inference

Causal Discovery over Clusters of Variables in Non-Markovian Systems

Tara Anand, Adèle H Ribeiro, Jin Tian, George Hripcsak and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of Warwick National University of Singapore Indian Institute of Science Causal inference

Weighted Sampling for Online Causal Discovery

Arnab Bhattacharyya, Philips George John, Sayantan Sen, Naganand Yadati

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Max Planck Institute for Informa Saarland Causal inference

A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models

Yasmine Briefs, Markus Bläser

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026IST Austria Institute of Science and Technol Institute Of Science And Technol ISTA Causal inference

Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

Yiwen Qiu, Francesco Montagna, Shimeng Huang, Francesco Locatello

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Mohamed bin Zayed University of Causal inference

Identification and Bounding of Joint Expectation over Potential Outcomes

Yuta Kawakami, Jin Tian

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Sungkyunkwan Sung Kyun Kwan Causal inference

Joint Confounder Selection for Causal Mediation Analysis in High Dimensions

Chanmin Kim, Hyunwoo Kim

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Stanford U Southern California Causal inference

Statistical Inference in Causal Partial Identification under Smooth Densities

Sirui Lin, Zijun Gao, Jose Blanchet, Peter W Glynn

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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78%Highly rated

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NeurIPS 2026Institute of Science and Technol ISTA Causal inference

Causal learning with the invariance principle

Assuming acyclic, invariant causal relations across environments, two auxiliary environments identify arbitrary nonlinear causal graphs and enable correct counterfactual inference.

Francesco Montagna, Francesco Locatello

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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71%Highly rated

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NeurIPS 2026SpotlightInstitute of Science and Technol CuspAI / University of Amsterdam ISTA Causal inference

Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models

Hamiltonian Causal Models separate equations of motion from intervenable mechanisms and define causal effects as interventional path discrepancies, showing entropy production witnesses trajectory-level causal effects invisible to standard average treatment effects.

Dario Rancati, Max Welling, Francesco Locatello

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 6 of 20 reviewers recommend it

lenient 2/5

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80%Must read

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NeurIPS 2026Technical University of Denmark DTU U Copenhagen Causal inference

Coarsening Linear Non-Gaussian Causal Models with Cycles

Linear non-Gaussian cyclic models yield recoverable low-dimensional acyclic summaries representing observational equivalence classes, learnable in cubic time with sample complexity bounds.

Francisco Madaleno, Francisco Pereira, Alex Markham

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it

lenient 2/5

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83%Must read

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