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://github.com/causal-rl-anonymous/causal-rl

공식 코드

Causal Reinforcement Learning using Observational and Interventional Data

https://github.com/causalaibook/metricsmlnotebooks

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Applied Causal Inference Powered by ML and AI

https://github.com/causalailab/neuralcausalabstractions

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Neural Causal Abstractions

https://github.com/causalailab/neuralcausalmodels

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The Causal-Neural Connection: Expressiveness, Learnability, and Inference

https://github.com/causalbench/causalbench-starter

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CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

https://github.com/causalgraph/causalgraph

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causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs

https://github.com/causalgraph/causalgraph-ontology

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causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs

https://github.com/causallearningai/istant

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Smoke and Mirrors in Causal Downstream Tasks

https://github.com/causalml/boundsonfractionnegativelyaffected

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What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment

https://github.com/causalml/doublyvaliddoublysharpsensitivity

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Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding

https://github.com/causalml/longtermcausalinference

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Long-term Causal Inference Under Persistent Confounding via Data Combination

https://github.com/causalml/proximalrl

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Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes

https://github.com/causalmp/causalmp

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Can We Validate Counterfactual Estimations in the Presence of General Network Interference?

https://github.com/causalnlp/causal-cite

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CausalCite: A Causal Formulation of Paper Citations

https://github.com/causalnlp/cladder

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CLadder: Assessing Causal Reasoning in Language Models

https://github.com/causalnlp/corr2cause

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Can Large Language Models Infer Causation from Correlation?

https://github.com/causalsim/unbiased-trace-driven-simulation

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CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation

https://github.com/chongminggao/cirs-codes

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CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

https://github.com/chris-tran-16/CTL

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Learning Triggers for Heterogeneous Treatment Effects

https://github.com/chrisjtan/counter

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Counterfactual Explainable Recommendation

https://github.com/chrisjtan/gnn_cff

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Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

https://github.com/chrissie-law/causal-inference-for-recommendation

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A Survey on Causal Inference for Recommendation

https://github.com/christopher-br/cbrnet

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Using representation balancing to learn conditional-average dose responses from clustered data

https://github.com/chritoth/active-bayesian-causal-inference

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Active Bayesian Causal Inference

https://github.com/chritoth/bci-arco-gp

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Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders

https://github.com/chuanqichencc/cgnsde

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CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation

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

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Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa

https://github.com/claudiashi57/dragonnet

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Adapting Neural Networks for the Estimation of Treatment Effects

https://github.com/claudiashi57/nice

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Invariant Representation Learning for Treatment Effect Estimation

https://github.com/clinicalml/finding-decision-heterogeneity-regions

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Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance

https://github.com/clinicalml/structuredinference

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Deep Kalman Filters

https://github.com/corinne-rahel/networkaipw

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Treatment Effect Estimation with Observational Network Data using Machine Learning

https://github.com/crabtain959/synbeats

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Forecasting Algorithms for Causal Inference with Panel Data

https://github.com/cripac-dig/balanced-multimodal-rec

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Modality-Balanced Learning for Multimedia Recommendation

https://github.com/criteo-research/large-scale-ite-um-benchmark

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A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

https://github.com/crossrm/gsls

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Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inference

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.