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/rongguangw/flow-scm

공식 코드

Harmonization with Flow-based Causal Inference

https://github.com/rrrussell/caugnn

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Multivariate Time Series Forecasting with Transfer Entropy Graph

https://github.com/rsparapa/bnptools

공식 코드

Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learning

https://github.com/ruoqi-liu/DeepIPW

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When deep learning meets causal inference: a computational framework for drug repurposing from real-world data

https://github.com/ruoxuanxiong/federated-causal-inference

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Federated Causal Inference in Heterogeneous Observational Data

https://github.com/ryan-thompson/ProDAG.jl

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ProDAG: Projected Variational Inference for Directed Acyclic Graphs

https://github.com/sakshambassi/hd-causal-effect

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Learning high-dimensional causal effect

https://github.com/salesforce/causalai

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Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data

https://github.com/seabrapt/brain_underlying_structure_identification

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Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

https://github.com/seedatnabeel/te-cde

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Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

https://github.com/seekingdream/nmtsloth

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LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models

https://github.com/shaido987/alarm-rca

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An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks

https://github.com/sherrirose/ConditionalCohortSamples

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Consistent Estimation of Propensity Score Functions with Oversampled Exposed Subjects

https://github.com/shiningliang/Multi-level-Causality-Detection-Network

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A Multi-level Neural Network for Implicit Causality Detection in Web Texts

https://github.com/shlizee/TimeAwarePC

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Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC Algorithm

https://github.com/shunli-wang/ca-spacenet

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CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space

https://github.com/simonvino/DCRNN_brain_connectivity

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A Graph Neural Network Framework for Causal Inference in Brain Networks

https://github.com/siqixu/deepmed

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DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

https://github.com/sobalgi/rhognf

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$ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows

https://github.com/softsys4ai/SCTL

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Scalable Causal Domain Adaptation

https://github.com/softsys4ai/unicorn

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Unicorn: Reasoning about Configurable System Performance through the lens of Causality

https://github.com/sohaib730/causalml

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Counterfactual Prediction Under Selective Confounding

https://github.com/sokbae/ciccr

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Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions

https://github.com/sokbae/persuasio

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Identifying the Effect of Persuasion

https://github.com/sokbae/replication-junlee-jbes

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Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions

https://github.com/sookyojeong/worst-ate

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Assessing External Validity Over Worst-case Subpopulations

https://github.com/srtaheri/LVMwithDoCalculus

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Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

https://github.com/stablemarkets/intro_bayesian_causal

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A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches

https://github.com/stat4reg/causal_cnn

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Convolutional neural networks for valid and efficient causal inference

https://github.com/stat4reg/dnncausal

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Convolutional neural networks for valid and efficient causal inference

https://github.com/stat4reg/hdim.ui

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Valid causal inference with unobserved confounding in high-dimensional settings

https://github.com/stevenn9981/ppac

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Debiasing Recommendation with Personal Popularity

https://github.com/strong-ai-lab/counterfactual-llm-inference

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Counterfactual Causal Inference in Natural Language with Large Language Models

https://github.com/superpig99/SBRL-HAP

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Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations

https://github.com/susantiyuni/path-to-causality

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Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery

https://github.com/suwonglab/causalegm

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CausalEGM: a general causal inference framework by encoding generative modeling

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