Causal AI Models (2026) | DoWhy, EconML, CausalML, causal-learn, DoubleML, Structural Causal Models & Causal Discovery

What Is Causal AI?

13 entries

Causal AI goes beyond finding correlations to model cause and effect: what happens if we intervene, and what would have happened otherwise (counterfactuals). Its foundations are structural causal models, causal graphs and the do-calculus, and its main tasks are causal discovery (learning structure from data), effect estimation (average and heterogeneous treatment effects) and counterfactual reasoning. It supports decisions, fairness, robustness and explanation where purely predictive models fall short.

01Causal AI (Wikipedia)Overview of causal AI. 02Causal inference (Wikipedia)Methods for drawing causal conclusions. 03Causal model (Wikipedia)Models of causal mechanisms. 04Structural causal model (Wikipedia)Pearl's framework of variables and structural equations. 05Do-calculus (Wikipedia)Rules for reasoning about interventions. 06Directed acyclic graph (Wikipedia)The graph structure behind causal diagrams. 07Causal discovery (Wikipedia)Learning causal structure from data. 08Uplift modelling (Wikipedia)Predicting the incremental effect of a treatment. 09Propensity score matching (Wikipedia)Adjusting for confounding in observational data. 10Instrumental variables (Wikipedia)Estimating effects with an instrument. 11Difference in differences (Wikipedia)Quasi-experimental estimation over time. 12Regression discontinuity (Wikipedia)Effects from a cutoff in assignment. 13Probabilistic causation (Stanford Encyclopedia)Philosophical background.

Causal Inference & Effect Estimation Libraries

14 entries

Estimate treatment effects and test assumptions.

14PyWhyOpen-source ecosystem for causal machine learning in Python. 15DoWhyEnd-to-end causal inference: model, identify, estimate and refute. 16DoWhy on GitHubSource code. 17EconMLHeterogeneous treatment effects with machine learning. 18CausalML (Uber)Uplift modeling and causal inference methods. 19DoubleMLDouble/debiased machine learning in Python and R. 20CausalPyQuasi-experimental causal inference with Bayesian methods, from PyMC Labs. 21grf (Generalized Random Forests)Forest-based estimation of heterogeneous effects in R. 22CausalImpact (Google)Estimate the impact of an intervention on a time series. 23Google MeridianMarketing mix modelling with causal inference. 24TigramiteCausal discovery and inference for time series. 25CausalNexBayesian networks for causal reasoning. 26pgmpyProbabilistic graphical models and causal inference. 27PyroProbabilistic programming with support for causal reasoning.

Causal Discovery Tools

12 entries

Learn causal structure from data.

28causal-learnPython port of the Tetrad causal discovery algorithms. 29TetradCarnegie Mellon's causal discovery software. 30PyWhy dodiscoverCausal discovery algorithms in the PyWhy ecosystem. 31pywhy-graphsCausal graph data structures for PyWhy. 32LiNGAMNon-Gaussian causal discovery. 33NOTEARSContinuous optimisation for structure learning. 34Causal Discovery ToolboxAlgorithms for causal graph recovery. 35gCastle / trustworthyAI (Huawei)Causal structure learning toolchain. 36Causica (Microsoft)Deep end-to-end causal inference. 37ShowWhy (archived)Microsoft's no-code causal analysis tool; the repository is archived. 38pcalg (R)PC and related algorithms in R. 39bnlearn (R)Bayesian network structure learning in R.

Causal Reasoning in LLMs

11 entries

Can language models reason about cause and effect?

40Causal LLMs: The Next Frontier of Agentic ReasoningOur guide to causal reasoning benchmarks, LLM-assisted causal discovery, causal analysis agents and causal designs for agents. 41Causal Reasoning and Large Language Models: Opening a New FrontierEvaluates LLMs on causal tasks and discusses implications. 42Can Large Language Models Infer Causation from Correlation? (Corr2Cause)Benchmark showing limits of LLM causal inference. 43CLadder: Assessing Causal Reasoning in Language ModelsBenchmark based on Pearl's ladder of causation. 44CLadder on GitHubBenchmark data and code. 45Corr2Cause on GitHubData and code. 46Large Language Models for Causal DiscoveryUsing LLMs to propose and assess causal structure. 47Causality for Machine LearningBernhard Schölkopf on why ML needs causality. 48Towards Causal Representation LearningLearning high-level causal variables from low-level data. 49Invariant Risk MinimizationLearning invariant predictors across environments. 50CausalNLP papers listReading list on causality and NLP.

Books, Courses & Research Groups

11 entries

Learn causal inference.

51Introduction to Causal Inference (Brady Neal)Free course with lecture notes and videos. 52Causal Inference for the Brave and TrueFree Python book. 53Causal Inference: The MixtapeFree book on causal inference for economists and social scientists. 54Causal Inference: What If (Hernán and Robins)Free book on causal inference. 55Elements of Causal Inference (Peters, Janzing, Schölkopf)Open-access book on foundations and learning algorithms. 56The Book of Why (Judea Pearl)Pearl's popular introduction to causal reasoning. 57Causality (Judea Pearl)The foundational text. 58A Crash Course in Causality (Coursera)University of Pennsylvania course. 59Microsoft Research: Causal inferenceResearch group behind DoWhy and EconML. 60Causal Artificial Intelligence Lab (Columbia)Elias Bareinboim's lab. 61UCLA Cognitive Systems LaboratoryJudea Pearl's group.

Commercial Platforms

1 entries

Vendor offerings.

62causaLensCausal AI and AI agents for enterprises.

9 entries

Neighbouring pages on this site.

63Machine LearningMachine learning hub. 64Explainable AI FrameworksFrameworks for explaining models. 65Python Libraries for XAIPython libraries for explainability. 66Reinforcement LearningReinforcement learning. 67World Action ModelsPredictive world models for agents. 68Large Reasoning ModelsReasoning-focused models. 69Data ScienceData science hub. 70Data AnalyticsData analytics hub. 71Artificial IntelligenceAI hub.

Frequently Asked Questions

What is causal AI?

AI that models cause and effect rather than only correlation, so it can answer what happens if we intervene and what would have happened otherwise.

How is causal inference different from machine learning prediction?

Prediction finds patterns that hold in the observed data; causal inference estimates the effect of an action, which requires assumptions about the data-generating process such as a causal graph and no unmeasured confounding.

Which library should I start with?

DoWhy for the full workflow of modelling, identifying, estimating and refuting an effect, EconML or CausalML for heterogeneous effects, and causal-learn or Tigramite for causal discovery.

Can large language models reason causally?

They can handle many causal questions, but benchmarks such as Corr2Cause and CLadder show clear limits, especially inferring causation from correlation; treat LLM-proposed causal graphs as hypotheses to test.

How current is this list?

Links were checked in October 2026. Archived projects are labelled; sites that block automated checks are kept and may need a manual click.

Causal AI Models: Causal Inference, Discovery, Libraries & Learning Resources