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 876–900 of 1722 papers

TitleStatusHype
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback—0
Causal Inference out of Control: Estimating the Steerability of Consumption—0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint—0
Toward a Theory of Causation for Interpreting Neural Code Models—0
A Fast Bootstrap Algorithm for Causal Inference with Large DataCode0
Causal Estimation of Exposure Shifts with Neural NetworksCode0
Causal Confirmation Measures: From Simpson's Paradox to COVID-19—0
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
How to select predictive models for causal inference?—0
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model—0
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization—0
Convolutional neural networks for valid and efficient causal inferenceCode0
Proximal Causal Learning of Conditional Average Treatment Effects—0
Causal Inference under Data Restrictions—0
Non-parametric identifiability and sensitivity analysis of synthetic control models—0
Optimal Transport for Counterfactual Estimation: A Method for Causal InferenceCode0
Causal Falsification of Digital TwinsCode0
Interacting Treatments with Endogenous Takeup—0
Subset verification and search algorithms for causal DAGsCode0
Causal Inference for Recommendation: Foundations, Methods and Applications—0
Action needed to make carbon offsets from tropical forest conservation work for climate change mitigation—0
Continual Causal Effect Estimation: Challenges and Opportunities—0
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All datasetsIHDPJobsIDHP

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