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/lcastri/roscausal

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ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications

https://github.com/lgresele/structural-causal-marginal

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Causal Inference Through the Structural Causal Marginal Problem

https://github.com/lguerdan/icml24_predictive_performance_comparison_dps

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Predictive Performance Comparison of Decision Policies Under Confounding

https://github.com/liangyuanhu/ML-DL-TEH

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Estimating heterogeneous survival treatment effect in observational data using machine learning

https://github.com/lingfeiwang/findr

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Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

https://github.com/liuqs111/DNA-SE

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DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation

https://github.com/lixiang-222/CIDGMed

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CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning

https://github.com/lixiang-222/causalmed

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CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state

https://github.com/lixiang-222/dgmed

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CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning

https://github.com/liyuhui-12/drmfordg

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Direct-Effect Risk Minimization for Domain Generalization

https://github.com/lopezpaz/causation_learning_theory

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Towards a Learning Theory of Cause-Effect Inference

https://github.com/lucasdelara/PI-Fair

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Transport-based Counterfactual Models

https://github.com/mahp-scut/sticm

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Spatiotemporal information conversion machine for time-series prediction

https://github.com/majianthu/transferentropy

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Estimating Transfer Entropy via Copula Entropy

https://github.com/marti5ini/ci-over-time

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A Practical Approach to Causal Inference over Time

https://github.com/martinascauda/OrdinalEffects

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A Latent Causal Inference Framework for Ordinal Variables

https://github.com/mas-takayama/LLM-and-SCD/tree/main/codes

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

https://github.com/mas-takayama/llm-and-scd

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

https://github.com/matthewvowels1/TVAE_release

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Targeted VAE: Variational and Targeted Learning for Causal Inference

https://github.com/matthewvowels1/slem

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SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling

https://github.com/mattyjsmith/CVTMLE

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Performance of Cross-Validated Targeted Maximum Likelihood Estimation

https://github.com/mauriciogtec/weather2vec-reproduce

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Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies

https://github.com/max-ilse/CausalReduction

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Combining Interventional and Observational Data Using Causal Reductions

https://github.com/maximilianahrens/data

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Bayesian Topic Regression for Causal Inference

https://github.com/maximilianpi/pichler-and-hartig-2022

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Machine Learning and Deep Learning -- A review for Ecologists

https://github.com/mdk31/causalbootstrap

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A Fast Bootstrap Algorithm for Causal Inference with Large Data

https://github.com/meirtz/focusonslang-toolbox

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SLANG: New Concept Comprehension of Large Language Models

https://github.com/menghsuanhsieh/tangential-wasserstein-projection

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Tangential Wasserstein Projections

https://github.com/mhinne/BNQD

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Bayesian nonparametric discontinuity design

https://github.com/michaellli/neymanmlcode

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Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

https://github.com/michaelpollmann/spatialtreat-example

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Causal Inference for Spatial Treatments

https://github.com/michelesantacatterina/KOM

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Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching

https://github.com/microsoft/causica

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Towards Causal Foundation Model: on Duality between Causal Inference and Attention

https://github.com/microsoft/cf-ode

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Predicting the impact of treatments over time with uncertainty aware neural differential equations

https://github.com/microsoft/dowhy

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DoWhy: An End-to-End Library for Causal Inference

https://github.com/mikkelpm/svma_iv

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Instrumental Variable Identification of Dynamic Variance Decompositions

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