Papers with Code 아카이브

Causal Inference

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 3
평가표: 3
보존된 평가 기록: 20

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

<span style="color:grey; opacity: 0.6">( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/mvdschaar/mlforhealthlabpub

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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

https://github.com/2024sig/sig

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SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs

https://github.com/3wen/counterfactual-estimation-optimal-transport

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Optimal Transport for Counterfactual Estimation: A Method for Causal Inference

https://github.com/99Catherine99/peer-induced-fairness

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Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing

https://github.com/AIML-K/ActiveBayesianCausal

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ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments

https://github.com/AIandGlobalDevelopmentLab/causalimages-software

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CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

https://github.com/Aiying0512/psi-LiNGAM

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Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)

https://github.com/AlexImmer/loci

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On the Identifiability and Estimation of Causal Location-Scale Noise Models

https://github.com/AliciaCurth/CATENets

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On Inductive Biases for Heterogeneous Treatment Effect Estimation

https://github.com/Baran-phys/DynamicFormer

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Can Transformers Do Enumerative Geometry?

https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation

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Causal Unsupervised Semantic Segmentation

https://github.com/CausalAILab/ListConditionalIndependencies

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Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies

https://github.com/CausalML/LocalizedDebiasedMachineLearning

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Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond

https://github.com/CongWang141/JMP

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Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis

https://github.com/Cqyiiii/Practical-Performative-Policy-Learning-PPPL

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Practical Performative Policy Learning with Strategic Agents

https://github.com/D2I-Group/matmcd

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Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery

https://github.com/DM2-ND/CFLP

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Learning from Counterfactual Links for Link Prediction

https://github.com/DUT-lujunyu/Debias

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Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect

https://github.com/DarkEyes/VLTimeSeriesCausality

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Variable-lag Granger Causality for Time Series Analysis

https://github.com/DataResponsibly/CIFRank

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Causal intersectionality for fair ranking

https://github.com/Dingseewhole/CI_LightGCN_master

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Causal Incremental Graph Convolution for Recommender System Retraining

https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs

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A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

https://github.com/EliotZhu/TCS

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CDS -- Causal Inference with Deep Survival Model and Time-varying Covariates

https://github.com/EliotZhu/Targeted-Survival

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Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis

https://github.com/Exploration-Lab/COLD

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COLD: Causal reasOning in cLosed Daily activities

https://github.com/Gaochenyin/SurrConformalDR

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On the Role of Surrogates in Conformal Inference of Individual Causal Effects

https://github.com/Healthy-AI/TreatmentExploration

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Learning to search efficiently for causally near-optimal treatments

https://github.com/Humboldt-WI/response-dependent-costs

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Targeting customers under response-dependent costs

https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework

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Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis

https://github.com/IBM-HRL-MLHLS/IBM-Causality-Benchmarking-Framework

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Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis

https://github.com/IBM/causallib

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Adversarial Balancing for Causal Inference

https://github.com/IDSIA-papers/2020-PGM-structural

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Structural Causal Models Are (Solvable by) Credal Networks

https://github.com/IliasZadik/double_orthogonal_ml

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Orthogonal Machine Learning: Power and Limitations

https://github.com/Influence-Disinformation-Networks/PNAS-Narrative-Networks

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Automatic Detection of Influential Actors in Disinformation Networks

https://github.com/JanRabenseifner/Causal-Propensity-Calibration

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Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators

https://github.com/JianqiaoMao/mechanism-learn

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Mechanism learning: Reverse causal inference in the presence of multiple unknown confounding through front-door causal bootstrapping

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