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 · Open MIND · 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…

• 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…

• Evaluating the Adoption of Community Health Centre Systems in Ghana: A Quasi-Experimental Methodological Assessment
  Ama Serwaa Mensah, Kwame Osei · Open MIND · Oct 2, 2026

  { "background": "Community health centres are a cornerstone of primary healthcare delivery in many African nations, yet robust methodological frameworks for evaluating their systematic adoption are lacking. Existing assessments often rely o…

• Evaluating the Adoption of Community Health Centre Systems in Ghana: A Quasi-Experimental Methodological Assessment
  Ama Serwaa Mensah, Kwame Osei · Zenodo (CERN European Organ... · Oct 2, 2026

  { "background": "Community health centres are a cornerstone of primary healthcare delivery in many African nations, yet robust methodological frameworks for evaluating their systematic adoption are lacking. Existing assessments often rely o…

• Evaluating Emergency Care Systems in Kenya: A Difference-in-Differences Analysis of Clinical Outcomes
  Wanjiku Mwangi · Open MIND · Aug 23, 2026

  { "background": "Emergency care systems in sub-Saharan Africa are underdeveloped, with limited evidence on the impact of formalising these services on patient outcomes. Robust evaluations of health system interventions in low-resource setti…

• Evaluating Emergency Care Systems in Kenya: A Difference-in-Differences Analysis of Clinical Outcomes
  Wanjiku Mwangi · Zenodo (CERN European Organ... · Aug 23, 2026

  { "background": "Emergency care systems in sub-Saharan Africa are underdeveloped, with limited evidence on the impact of formalising these services on patient outcomes. Robust evaluations of health system interventions in low-resource setti…

• Data-Poisoning Audits for Causal Effect Estimation
  Kwangho Kim · arXiv · Jul 22, 2026

  Observational causal analyses increasingly pool records across sites, vendors, and collection systems, creating vulnerability to append-only attacks in which plausible records are strategically selected to alter a reported treatment effect.…

• Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference
  Masahiro Kato, Taka Kato · arXiv · Jul 20, 2026

  We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential outcome framework. In the two-step method, vector search retrieves action-sp…

• An Adjoint-Sensitivity Framework for Lost-in-the-Middle Phenomena in Causal Residual Transformers
  Cheng Huan, Hongwei Yuan · arXiv · Jul 20, 2026

  We develop an adjoint-sensitivity framework for positional influence in causal residual Transformers and separate unconditional analytic results from conditional boundary-shape conclusions. The principal unconditional theorem is the residua…

• Relaxing Faithfulness with Intervention-Only Causal Discovery
  Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler · arXiv · Jul 13, 2026

  Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed …

• DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
  Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui et al. · arXiv · Jul 13, 2026

  Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs). In…

• CDFM: Towards a General-Purpose Causal Discovery Foundation Model
  Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen et al. · arXiv · Jul 13, 2026

  Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines. Over the past decades, numerous algorithms have been developed to tackle this challeng…

• Deep Gaussian Processes on Directed Acyclic Graphs
  Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen, Theodoros Damoulas · arXiv · Jul 10, 2026

  Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity levels; and in gene-reg…

• A Statistical Test for the Benefits of Personalizing Interventions
  Zhaoqi Li, Emma Brunskill · Science · Jul 9, 2026

  From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's potential benefits with its possible increased cost and fra…

• Structure Learning on Clustered Data
  Ryan Thompson, Matt P. Wand, Veerabhadran Baladandayuthapani · arXiv · Jul 9, 2026

  Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely homogeneous population, precluding their application to cluster…

• Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
  Yurui Zheng, Ying Jin · arXiv · Jul 2, 2026

  Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making. To ensure reliability with imperfect predictions, uncertainty quantification methods such as conformal prediction build prediction …

• Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence
  Andreas Koukorinis, Ricardo Silva · arXiv · Jun 29, 2026

  We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.

• Non-parametric recovery of causal diffusion mechanisms from steady-state observations
  Richard Schwank, Mathias Drton · arXiv · Jun 29, 2026

  We consider sparse multivariate stochastic systems that evolve in continuous time according to a causal mechanism and present methodology to recover the system's time-infinitesimal transition mechanism from mere cross-sectional data. This o…

• Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts
  Yuanyuan Wang, Wenjie Wang, Haoxuan Li, Mingming Gong et al. · arXiv · Jun 26, 2026

  Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent stochastic differential equation (SDE) models remains largely o…

• A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Support
  David Zapata Gonzalez · arXiv · Jun 23, 2026

  The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc explainability methods and inherently interpretable approaches shed light on mode…

• The Representational Limit of Scalar Interactions: An Interventional Decomposition
  Potito Aghilar, Sabino Roccotelli, Stanislao Fidanza, Vito Walter Anelli et al. · arXiv · Jun 17, 2026

  Signed pairwise interaction scores fundamentally conflate uniqueness (U), redundancy (R), and synergy (S). We prove this on a minimal 3-way XOR structural causal model: faithful indices such as Shapley-Taylor return zero per pair, whereas p…

• Wasserstein Policy Learning for Distributional Outcomes
  Yiyan Huang, Cheuk Hang Leung, Qi Wu, Zhiheng Zhang · arXiv · Jun 17, 2026

  Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates to treatment that maximizes the empirical welfare defined as…

• Balanced Twins: Causal Inference on Time Series with Hidden Confounding
  Ouali Maha, Ghattas Badih, Flachaire Emmanuel, Charpentier Philippe et al. · arXiv · Jun 17, 2026

  Accurately estimating treatment effects in time series is essential for evaluating interventions in real-world applications, especially when treatment assignment is biased by unobserved factors. In many practical settings, interventions are…

• Tensor-based second-order causal discovery
  Nathan Ouyang, Kexin Wan, Anna Seigal · arXiv · Jun 16, 2026

  Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its input is a tensor obtained from the covariance matrices of ob…

• Fast Nonparametric Conditional Independence Testing via Two-Stage Regression
  Eric V. Strobl · arXiv · Jun 16, 2026

  Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships. We intr…

• Proximal Mediation Analysis with Hidden Recanting Witnesses
  Sihan Wu, Yang Bai, Yifan Cui · arXiv · Jun 16, 2026

  Mediation analysis is essential for decomposing the causal effect of a treatment into direct and indirect pathways. However, many practical settings rely on the stringent assumption that recanting witnesses, defined as treatment-induced med…

• FoundCause: Causal Discovery with Latent Confounders from Observational Data
  Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan · arXiv · Jun 16, 2026

  Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an amortized causal discovery model trained entirely on syntheti…

• Kernel-Based Functional Balancing for Causal Inference with Compositional Treatments
  Sungbum Kim, Jiayi Wang · arXiv · Jun 15, 2026

  We study causal effect estimation with compositional treatments, where the exposure lies on a simplex and the estimand is defined over compositions rather than scalar or binary values. By considering a projection of the average potential ou…

• Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data
  Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard et al. · arXiv · Jun 15, 2026

  The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets. However, generating high-utility synthetic data often carries …

• Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining
  Zhengyuan Gao · arXiv · Jun 15, 2026

  Causal self-attention is a coupling mechanism: each token's hidden state is updated by a learned mixture of preceding tokens at the same timescale. This paper asks whether a second, temporally slower coupling-a slow sub-system operating on …