SOTAVerified

Causal Inference

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Papers

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Showing 1076–1100 of 1722 papers

TitleStatusHype
Causal discovery for observational sciences using supervised machine learningCode0
Off-Policy Evaluation with Policy-Dependent Optimization Response—0
Predicting the impact of treatments over time with uncertainty aware neural differential equationsCode0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender SystemsCode0
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data—0
Partial Identification with Noisy Covariates: A Robust Optimization Approach—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics—0
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows—0
Long-term Causal Inference Under Persistent Confounding via Data CombinationCode0
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression—0
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace—0
Validating Causal Inference Methods—0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
Evaluation Methods and Measures for Causal Learning Algorithms—0
Causal Inference Using Tractable Circuits—0
Deep End-to-end Causal Inference—0
Causal Inference Through the Structural Causal Marginal ProblemCode0
Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts—0
Heterogeneous Peer Effects in the Linear Threshold ModelCode0
Promises and Challenges of Causality for Ethical Machine Learning—0
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects—0
Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery—0
Symbiotic bacterial network structure involved in carbon and nitrogen metabolism of wood-utilizing insect larvae—0
Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified