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/heinerkremer/functional-gel

공식 코드

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

https://github.com/heyyyyyyg/cfie

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Uncovering Main Causalities for Long-tailed Information Extraction

https://github.com/hku-medai/mult-ehr

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Multi-task Heterogeneous Graph Learning on Electronic Health Records

https://github.com/hkust-knowcomp/cola

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COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

https://github.com/hlzhang109/transtee

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Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation

https://github.com/holyshun/GP-PLM

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Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

https://github.com/honda-research-institute/wellbeing-trust-model

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Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility

https://github.com/hongmingli1995/cr-vae

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Causal Recurrent Variational Autoencoder for Medical Time Series Generation

https://github.com/huage001/linfusion

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LinFusion: 1 GPU, 1 Minute, 16K Image

https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle

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Masked Gradient-Based Causal Structure Learning

https://github.com/huckiyang/obs-causal-q-network

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Training a Resilient Q-Network against Observational Interference

https://github.com/hwdance/cocycles

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Causal Inference with Cocycles

https://github.com/hwj20/apex_exp

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APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight

https://github.com/ictnlp/capt

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Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

https://github.com/idegtiar1/CCDS

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Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid

https://github.com/idsia-papers/2021-neuripswhy-causalem

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Causal Expectation-Maximisation

https://github.com/idsia-papers/2023-ijar-bias-hybrid

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Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data

https://github.com/idsia-papers/causal-llms

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Zero-shot Causal Graph Extrapolation from Text via LLMs

https://github.com/igbucur/BFCS

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Large-Scale Local Causal Inference of Gene Regulatory Relationships

https://github.com/imkemayer/MissDeepCausal

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MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models

https://github.com/ioanabica/Time-Series-Deconfounder

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

https://github.com/ioannislivieris/C-XGBoost

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C-XGBoost: A tree boosting model for causal effect estimation

https://github.com/irenetubikanec/mmlh

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Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

https://github.com/iron13/dvae.civ

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Learning Conditional Instrumental Variable Representation for Causal Effect Estimation

https://github.com/iydk/vaki_diagnosis_ml4h

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Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury

https://github.com/j0hngou/LLMCWM

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Language Agents Meet Causality -- Bridging LLMs and Causal World Models

https://github.com/jaabmar/RAMEN

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Doubly robust identification of treatment effects from multiple environments

https://github.com/jackkuipers/ibge

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The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions

https://github.com/jacobmchen/proximal_w_text

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Proximal Causal Inference With Text Data

https://github.com/jaewoong-lee/sigir_2023_uctrl

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uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering

https://github.com/jakefawkes/dr_distributional_test

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Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects

https://github.com/jbccc/mech-interp-biostats

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics

https://github.com/jcreinhold/counterfactualms

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A Structural Causal Model for MR Images of Multiple Sclerosis

https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg

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When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

https://github.com/jmaasch/ldp

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Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

https://github.com/jmdvinodjmd/HLearner

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Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes

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