https://github.com/AliciaCurth/CATENets
공식 코드On Inductive Biases for Heterogeneous Treatment Effect Estimation
Papers with Code 아카이브
Heterogeneous Treatment Effect Estimation
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Heterogeneous treatment effect (HTE) estimation is the task of quantifying how treatment effects vary across different individuals or subgroups.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KimuraTian/sigir-eCom20-counterfactual-ltr-using-hte
공식 코드Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation
https://github.com/aliciacurth/cateselection
공식 코드https://github.com/aliciacurth/compcate
공식 코드https://github.com/chris-tran-16/CTL
공식 코드https://github.com/edgeslab/causal_feature_selection
공식 코드Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty
https://github.com/hlzhang109/transtee
공식 코드Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation
https://github.com/holyshun/GP-PLM
공식 코드https://github.com/khashayarkhv/np_inference_intrinsic
공식 코드https://github.com/pshuwei/rbf
공식 코드Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons
https://github.com/qinglong-tian/tabpfn_study
공식 코드https://github.com/superpig99/SBRL-HAP
공식 코드Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
https://github.com/swager/grf
공식 코드https://github.com/vanderschaarlab/compcate
공식 코드https://github.com/vdblm/CausalPFN
공식 코드CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
https://github.com/xnie/rlearner
공식 코드https://github.com/ychika/dpsw
공식 코드https://github.com/Statwolf/LocalLinearForest
https://github.com/clinicalml/cfrnet
Estimating individual treatment effect: generalization bounds and algorithms
https://github.com/ischeinfeld/natality
https://github.com/nlokeshiisc/pairnet_release
On Inductive Biases for Heterogeneous Treatment Effect Estimation
https://github.com/oddrose/cfrnet
Estimating individual treatment effect: generalization bounds and algorithms
https://github.com/rajkumarkarthik/mgrf-develop
https://github.com/saberpowers/causalLearning
Some methods for heterogeneous treatment effect estimation in high-dimensions
https://github.com/sschrod/bites
Estimating individual treatment effect: generalization bounds and algorithms
https://github.com/till-tietz/rcf
https://github.com/uber/causalml/blob/master/causalml/inference/meta/rlearner.py
https://github.com/vshirvaikar/rrcf
https://github.com/xinshuli2022/cite
Estimating individual treatment effect: generalization bounds and algorithms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.