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.
Get the latest Causal Inference papers in your inbox — free →Recent papers
- Evaluating Public Health Surveillance Systems in Nigeria: A Difference-in-Differences Analysis of Adoption Rates, 2000–2025Ngozi 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 Public Health Surveillance Systems in Nigeria: A Difference-in-Differences Analysis of Adoption Rates, 2000–2025Ngozi 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 AssessmentAma 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 the Adoption of Community Health Centre Systems in Ghana: A Quasi-Experimental Methodological AssessmentAma 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…
- Bidirectional Causal Inference for Binary Outcomes in the Presence of Unmeasured ConfoundingYafang Deng, Kang Shuai, Shanshan Luo · Statistica Sinica · Sep 11, 2026
Bidirectional causal relationships arising from mutual interactions between variables are commonly observed within biomedical, econometrical, and social science contexts. When such relationships are further complicated by unobserved factors…
- Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business ImpactMasahiro Kato, Daiki Honma, Taka Kato · arXiv · Sep 10, 2026
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estima…
- La teleología en la explicación científica contemporáneaEma Analy Takemura · Eikasía Revista de Filosofía · Sep 9, 2026
El resurgimiento de las propuestas teleológicas en el ámbito de la filosofía de la ciencia se debe, en gran medida, al problema que la noción de función biológica suscitó entre científicos y filósofos. Esta noción, cara a la ciencia biológi…
- Tensor Network Moral Graph Recovery of Discrete Probability DistributionsÁ. Troyano Olivas, Chi-Hang Fred Fung, Hans H. Brunner, Momtchil Peev et al. · arXiv · Sep 8, 2026
We present a method for recovering the moral graph of a causal DAG from a probability distribution over discrete variables, using fully connected tensor networks (FCTNs) with nuclear-norm-regularized bond corrections. Each bond matrix is pa…
- InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World ModelingZihao Yang, Zijia Wang, Zhiqiu Huang · arXiv · Sep 7, 2026
Multimodal large language models often capture visual-linguistic correlations but struggle to predict how local visual interventions propagate and affect downstream answers. We introduce InfluenceField, an intervention-aware latent field in…
- Mechanisms Linking Counselling Psychology Access to Household Welfare in Kenya: Multilevel Causal Mediation AnalysisKiprotich Kipkemboi Rono, Amina Hassan Abdi · Zenodo (CERN European Organ... · Sep 7, 2026
Counselling psychology services are expanding across Kenya, yet evidence linking their uptake to household welfare remains largely associational and atheoretical. Moving beyond a generic treatment-effect question, the proposed framework dis…
- Causal DAG Identification for Count Data via Poisson Thinning Structural Equation ModelsPenggang Gao, Ming Cai, Hisayuki Hara · arXiv · Sep 5, 2026
Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited. The Poisson branching structural causal model (PB-SCM…
- Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless NetworksAbdessamed Qchohi, Jessica Moysen Cortes, Matteo Zecchin · arXiv · Sep 4, 2026
Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined …
- Causal Foundation ModelsChristopher Stith, Hossein Rahmani, Jesse C. Cresswell · arXiv · Sep 2, 2026
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, an…
- I-FLOP: Fast Learning of Order and Parents from Interventional DataLiuting Chen, Alex Markham · arXiv · Aug 28, 2026
We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wienöbst et al. (2026) from observational to interventional data. In particular, we use the interventional BIC score of Hauser and Bühlmann (2012), adapt…
- Incremental Recommendation via Causal ModelsAthanasios Vlontzos, David Gustafsson, Michael O'Riordan, Ciarán M. Gilligan-Lee · arXiv · Aug 27, 2026
Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no incremental value and displaces other recommendations that could. We address this by extendin…
- Scalable and Versatile Identification for Hierarchical Structural Causal Models: A New Look at Project STARJanis Aiad, Aghiles Drali, Aymen El Ouadrhiri, Anass Ettahiri et al. · arXiv · Aug 25, 2026
The STAR (Student-Teacher Achievement Ratio) experiment (1985, Tennessee, USA) is a landmark hierarchical dataset designed to assess the impact of class size on student outcomes, with observations nested within classes. To encode class-leve…
- Revelation ControlQinyou Wang · arXiv · Aug 24, 2026
Revelation Control is the problem of choosing priced interventions that reveal hidden state only insofar as the revealed distinctions can change a consequential decision, while accounting separately for any useful progress created by the in…
- Evaluating Emergency Care Systems in Kenya: A Difference-in-Differences Analysis of Clinical OutcomesWanjiku 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 OutcomesWanjiku 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…
- A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-MakingWenxin Zhang, Rachael Phillips, Mark van der Laan · arXiv · Aug 19, 2026
Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments. However, unlike treatments, their impact on health outcomes is generally indirect because measuring d…
- Causal Generalization of Continuous Treatment Effects under Covariate ShiftJay Jojo Cheng, Guanhua Chen · arXiv · Aug 19, 2026
Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population. We study a covariate-shift setting in which co…
- Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge RegressionSambit Mishra, Urbashi Mitra · arXiv · Aug 17, 2026
Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent and per-problem hyperparameter tuning of continuous-optimizati…
- A Difference-in-Differences Model for Manufacturing Systems Efficiency: A Methodological Evaluation of South African Plants (2000–2024)Pieter van der Merwe, K. Mokoena, Anika Pretorius, Thandiwe Nkosi · Open MIND · Aug 17, 2026
{ "background": "Evaluating the impact of technological and managerial interventions on manufacturing systems efficiency requires robust quasi-experimental methods. The difference-in-differences (DiD) model is widely applied in econometrics…
- How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAMJikai Jin · arXiv · Aug 16, 2026
We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that independent non-Gaussian disturbances identify the direction, but does not quantify the d…
- Chance-constrained selection of sequential intervention strategies from counterfactual estimatesMinkyoung Kim, Beakcheol Jang · arXiv · Aug 13, 2026
Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget. Choosing among them calls for the outcome and the cumulative cost each would produce, cou…
- General Probabilities of Causation with Causal KnowledgeXin Shu, Zhen Lei, Ang Li · arXiv · Aug 12, 2026
Probabilities of causation (PoCs) characterize individual causal responses that cannot be directly observed and therefore generally require partial identification. Tian and Pearl first derived theoretically sharp bounds for binary PoCs, inc…
- Conditional Independence Tests for Constraint-Based Causal Discovery: A SurveyPavel Averin, Theodoros Moysiadis, Ioannis Katakis · arXiv · Aug 11, 2026
Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decis…
- When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMsOmatharv Bharat Vaidya, Connor Thomas Jerzak, Zayne Rea Sprague, Fangcong Yin et al. · arXiv · Aug 4, 2026
Self-consistency assumes the most frequent answer among sampled reasoning traces is the most reliable, but this can fail in causal reasoning: samples often repeat the same confounding error, and votes fragment across multiple valid answers,…
- Causal Inference with Unstructured OutcomesKevin Christian Wibisono, Yixin Wang · arXiv · Aug 4, 2026
Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives. Modern studies increasingly ask causal questions about outcomes with richer form, su…
- Calibrated Bayesian Inference for Stochastic Intervention EffectsTyler M. Schmidt, Nathan B. Wikle · arXiv · Aug 3, 2026
Causal inference increasingly extends beyond classical causal effects defined by deterministic treatment assignments, such as the average treatment effect, to stochastic intervention effects that can weaken positivity requirements and offer…