Latest Causal Inference Research Papers

The newest Causal Inference papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Causal Inference so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Evaluating Public Health Surveillance Systems in Nigeria: A Difference-in-Differences Analysis of Adoption Rates, 2000–2025
  Ngozi Eze, Ibrahim Suleiman, Adebayo Adeyemi, Chinwe Okonkwo · Zenodo (CERN European Organ... · Dec 13, 2026

  Public health surveillance systems are critical for early disease detection and response, yet their adoption and effectiveness in resource-limited settings remain inadequately quantified. Existing evaluations often lack robust counterfactua…

• Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.
  Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi · Policy Research Journal · Nov 3, 2026

  Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomic…

• Doubly Robust Deep Kernel Tests for Structured Outcomes
  Robin Leman, Bariscan Bozkurt, Sofiane Tanji, Arthur Gretton et al. · arXiv · Oct 27, 2026

  We propose a test of whether two interventions induce the same outcome distribution when outcomes are structured and high-dimensional, such as images, text and graphs. We represent the interventional distributions as mean embeddings on lear…

• Inference for Causal Path-Specific Effects in Longitudinal Studies with Repeated Treatments and Multiple Mediators
  Xiaxian Ou, Xinwei He, Razieh Nabi · arXiv · Oct 25, 2026

  In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates. We develop semiparametric inference for path-specific effects in this setting, allowing the number, type, and orde…

• Causal Inference in Metric Spaces via Distance Profiles
  Zheye Song, Zhiheng Zhang · arXiv · Oct 24, 2026

  Modern interventions increasingly produce object-valued outcomes, such as distributions, covariance matrices, networks, and trees. Existing causal frameworks often exploit special geometry such as one-dimensional Wasserstein quantiles or ge…

• Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations
  Jungang Zou, Joseph E. Schwartz, Roderick J. A. Little, Nathalie Moise et al. · arXiv · Oct 22, 2026

  The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome. We assess whether its findings can be extended to broader pati…

• Learning to Be Monitored: Causal Bounds and Intervention-Aware Post-Training Recipes for Chain-of-Thought Monitorability
  Qiang Fu, Xinyue Li, Andre Wibisono, Arman Cohan · arXiv · Oct 22, 2026

  Chain-of-thought (CoT) monitoring aims to detect potentially harmful behaviors in large language models (LLMs) from their CoTs. Intervention-based evaluations measure this property by asking whether a monitor can detect the role of an inter…

• The Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits
  Sharan Sahu, Martin T. Wells · arXiv · Oct 21, 2026

  Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards. However, decision-makers also require reliable inference on current treatment effects: differences between intervention and reference-arm mean r…

• The Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits
  Sharan Sahu, Martin T. Wells · arXiv · Oct 21, 2026

  Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards. However, decision-makers also require reliable inference on current treatment effects: differences between intervention and reference-arm mean r…

• When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
  Cong Cao · arXiv · Oct 21, 2026

  Learned representations of high-dimensional multimodal health records are increasingly used to adjust for confounding, yet they are usually trained to reconstruct the data rather than preserve the information that causal identification requ…

• Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression
  Daniel Klippert, Quang Nhat Huynh, Alexander Marx · arXiv · Oct 21, 2026

  Residual-based causal discovery assumes that each variable is generated from its parents and an independent noise source. It estimates this noise and tests its independence from candidate causes. For identifiable functions such as additive-…

• Backtracking Direct, Indirect and Total Effects
  Jose M. Peña · arXiv · Oct 20, 2026

  Natural direct and indirect effects are defined through cross-world interventional counterfactuals. We propose an alternative construction based on backtracking counterfactuals. Rather than intervening on the treatment or mediator, we use a…

• Debiased Prior-Data Fitted Networks for Amortized Bayesian Semi-Parametric Inference
  Valentyn Melnychuk, Emil Javurek, Stefan Feuerriegel · arXiv · Oct 20, 2026

  Prior-data fitted networks (PFNs) have recently gained popularity as an end-to-end approach to amortized Bayesian inference. Yet, their statistical behavior for semi-parametric targets, such as means under missingness or causal effects, rem…

• What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
  Masaaki Takada, Ryusei Shingaki · arXiv · Oct 20, 2026

  Alarms in industrial systems and scientific instruments often demand immediate diagnosis despite scarce incident data. To guide intervention, an explanation must trace how changes in the data-generating process propagate through the causal …

• A plug-in estimator of the non-iterative conditional expectation g-formula using long short-term memory models
  Jing Li, Sophia M. Rein, Zilong Wang, Zixiao Wang et al. · arXiv · Oct 19, 2026

  The non-iterative conditional expectation (NICE) plug-in estimator of the g-formula can be used to estimate the causal effects of sustained treatment strategies, even in the presence of treatment-confounder feedback, under the assumptions o…

• Dirichlet-Tree Allocation Priors for Bayesian Causal Forests
  Javier Enrique Aguilar, Daniel Klippert, Yusufhan Balci, Alexander Marx · arXiv · Oct 19, 2026

  Bayesian Causal Forests (BCF) estimate heterogeneous treatment effects with a separate ensemble of treatment trees. The prior on the leaf values of the treatment trees determines whether sampling variability is attributed to the treatment e…

• WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting
  Arjun Ashok, Mihir Parmar, Palash Goyal, Bhavana Dalvi Mishra et al. · arXiv · Oct 19, 2026

  Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors. This enables scenario analysis: forecasting how a target responds to events that set cov…

• Causal Robustness from Synthetic Environments
  Linying Yang, Cyrill Scheidegger, Xinwei Shen, Peter Bühlmann · arXiv · Oct 19, 2026

  We study robust prediction when the training data come from a single observational distribution. Causality-oriented methods for robust prediction usually use heterogeneity across observed environments both to learn an invariant prediction f…

• Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy
  Yanlin Zhang, Sungyong Chung, Alireza Talebpour · arXiv · Oct 16, 2026

  Synthetic control methods estimate counterfactual outcomes by weighting untreated donor units to match a treated unit before treatment. In recent applications, however, the donor pool can be large, highly correlated, and short in pre-treatm…

• Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
  Yang Ni · arXiv · Oct 16, 2026

  Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions. Historically, causal discovery has relied primarily on conditional independence. This review focuses on modern causal…

• Conformal Policy Selection: When to Deploy AI Decisions for Targeted Intervention
  Junyu Cao, Ruijiang Gao, Maytal Saar-Tsechansky, Mingzhang Yin · arXiv · Oct 16, 2026

  Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly requires knowing not only whether a policy helps on average, bu…

• Unit-level aggregation breaks coverage in causal foundation models
  Gabe Schulman, Lucius E. J. Bynum, Yoav Wald · arXiv · Oct 16, 2026

  Causal foundation models (CFMs) show promising results for treatment-effect estimation, with unit-level estimates that are competitive on standard benchmarks. Many causal analyses, however, inform decisions through effects averaged over gro…

• The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
  Damian Machlanski, Spyridon Samothrakis, Paul Clarke · Machine Learning · Oct 16, 2026

  Abstract ML is playing an increasingly crucial role in estimating causal effects of treatments on outcomes from observational data. Many ML methods (‘causal estimators’) have been proposed for this task. All of these methods, as with any ML…

• Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths
  Joran Michiels, Anis Ismail, Maarten De Vos, Alejandro Sifrim · arXiv · Oct 15, 2026

  Existing feature attribution methods that account for feature dependencies do not scale to high dimensions. We propose causal integrated gradients (CIG) and causal expected gradients (CEG), which extend integrated gradients to incorporate p…

• Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
  Dhanajit Brahma, Ruolin Meng, Lawrence Carin, Ricardo Henao · arXiv · Oct 14, 2026

  Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses unmeasured confounding through proxy variables, but existing proximal survival methods rely …

• Complete or Sparse: A Tale of Two Identifiabilities
  Junze Zhou, David Klindt · arXiv · Oct 14, 2026

  Understanding when neural networks learn interpretable representations is central to mechanistic interpretability. We study this through identifiability: when can supervised learning recover the true latent variables underlying the data? We…

• Testing Conditional Distribution Equality Using Generative Models
  Zekai Jin, Yi Zhang, Yun Yang, Xiaofeng Shao · arXiv · Oct 14, 2026

  Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a …

• Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
  Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta, Karthika Mohan · arXiv · Oct 13, 2026

  While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain identifiable (i.e., uniquely determined f…

• Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries
  Inderjeet Singh · arXiv · Oct 13, 2026

  Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize this limitation for embodied and tool-use AI. An adversary that matches the complete obser…

• Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees
  Hanna Benarroch, Jamal Atif, Olivier Cappé · arXiv · Oct 13, 2026

  Certified machine unlearning ensures the proximity between the unlearned model and a model retrained without the data to be forgotten. Extending this framework to sequential deletion requests, typically arriving as groups of variable size (…