Papers with Code 아카이브

Causal Inference

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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/saberpowers/causalLearning

Some methods for heterogeneous treatment effect estimation in high-dimensions

https://github.com/saccharomycetes/text-based-traffic-understanding

SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

https://github.com/sschrod/adbcr

Learning Representations for Counterfactual Inference

https://github.com/sschrod/bites

Estimating individual treatment effect: generalization bounds and algorithms

https://github.com/stablemarkets/StanCon2020_BayesCausal

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

https://github.com/sweichwald/coroICA-R

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

https://github.com/sweichwald/coroICA-matlab

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

https://github.com/tatsu432/DR-estimator-OPE-large-action

Diffusion Model in Causal Inference with Unmeasured Confounders

https://github.com/till-tietz/rcf

Generalized Random Forests

https://github.com/tmatcham/CausalBART

BART: Bayesian additive regression trees

https://github.com/tychen-SJTU/MECD-Benchmark

MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning

https://github.com/uber/causalml

CausalML: Python Package for Causal Machine Learning

https://github.com/uber/causalml/blob/master/causalml/inference/meta/rlearner.py

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

https://github.com/uber/causalml/blob/master/causalml/inference/nn/cevae.py

Causal Effect Inference with Deep Latent-Variable Models

https://github.com/uber/causalml/blob/master/causalml/inference/tf/dragonnet.py

Adapting Neural Networks for the Estimation of Treatment Effects

https://github.com/valentyn1997/au-cnfs

Counterfactual Mean Embeddings

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/ganite

https://github.com/vittorioorlandi/drbart

BART: Bayesian additive regression trees

https://github.com/vshirvaikar/rrcf

Generalized Random Forests

https://github.com/walter0807/repbelief

Language Models Represent Beliefs of Self and Others

https://github.com/wenjiewwj/clickbait

Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

https://github.com/xinshuli2022/cite

Estimating individual treatment effect: generalization bounds and algorithms

https://github.com/y0-causal-inference/y0

Surrogate Outcomes and Transportability

https://github.com/yaochenzhu/deep-deconf

Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization

https://github.com/yccm/diffpo

DiffPO: A causal diffusion model for learning distributions of potential outcomes

https://github.com/yunhao-tech/Counterfactual-Fairness

Counterfactual Fairness

https://github.com/zacharie12/zacharie

Unbiased Scene Graph Generation from Biased Training

https://github.com/zecevic-matej/tractable-neural-causal-model

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

https://github.com/zhangyk8/npDoseResponse

Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments

https://gitlab.com/dlr-dw/fastcav

Using Causal Analysis for Conceptual Deep Learning Explanation

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