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/jmdvinodjmd/HyperITE

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Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation

https://github.com/joaocarv/invariant-anomaly-detection

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Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective

https://github.com/johnchrishays/dml4ssi

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Double Machine Learning for Causal Inference under Shared-State Interference

https://github.com/joisino/poincare

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Poincare: Recommending Publication Venues via Treatment Effect Estimation

https://github.com/joisino/twinpaper

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Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling

https://github.com/jordanraykov/kernel-based-estimators-for-functional-causal-effects

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Kernel-based estimators for functional causal effects

https://github.com/joseffaghihi/a-fundamental-probabilistic-fuzzy-logic-framework-suitable-for-causal-reasoning

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A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal Reasoning

https://github.com/joshua-nugent/covid-mtp

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Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures

https://github.com/jpaillard/permucate

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Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence

https://github.com/jschweisthal/complexpartialidentif

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Learning Representations of Instruments for Partial Identification of Treatment Effects

https://github.com/jtcbrule/whittemore

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Whittemore: An embedded domain specific language for causal programming

https://github.com/jtextor/dagitty/blob/master/r/R/ci-tests.R

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A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data

https://github.com/juangamella/causal-chamber-paper

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The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology

https://github.com/jucheng1992/ctmle

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On Adaptive Propensity Score Truncation in Causal Inference

https://github.com/juhakarvanen/simulating_counterfactuals

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Simulating counterfactuals

https://github.com/juliamendelsohn/bridging-nations

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Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media

https://github.com/jvc2688/KeplerPixelModel

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Removing systematic errors for exoplanet search via latent causes

https://github.com/jvpoulos/rnns-causal

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RNN-based counterfactual prediction, with an application to homestead policy and public schooling

https://github.com/jzy95310/deep_sci

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Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments

https://github.com/kaihcohrs/dml-4-fluxes-chm

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Causal hybrid modeling with double machine learning

https://github.com/kaihcohrs/hybrid-q10-model-chm

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Causal hybrid modeling with double machine learning

https://github.com/kailiang-zhong/DESCN

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DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

https://github.com/kaushalpaneri/ode2scm

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Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

https://github.com/kazuyanagimoto/unitdid

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On Causal Inference with Model-Based Outcomes

https://github.com/kflijia/bijective_crossing_functions

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Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space

https://github.com/khashayarkhv/np_inference_intrinsic

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Non-Parametric Inference Adaptive to Intrinsic Dimension

https://github.com/koh-t/SC-CFR

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Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference

https://github.com/kolyanray/Bayesian-Causal-Inference

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Debiased Bayesian inference for average treatment effects

https://github.com/kunnao/CausalVIB

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Causal Effect Estimation using Variational Information Bottleneck

https://github.com/kurowasan/GraN-DAG

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Gradient-Based Neural DAG Learning

https://github.com/kyunghyuncho/2024-causal-inference-machine-learning

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A Brief Introduction to Causal Inference in Machine Learning

https://github.com/larslorch/stadion

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Causal Modeling with Stationary Diffusions

https://github.com/laurenok24/nsaqa

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Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment

https://github.com/lazycal/ame

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Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

https://github.com/lbynum/sequence-driven-scms

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Language Models as Causal Effect Generators

https://github.com/lcastri/PeopleFlow

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Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments

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