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 101–125 of 1722 papers

TitleStatusHype
Counterfactual Inference under Thompson Sampling—0
A Causal Inference Framework for Data Rich Environments—0
Identifying Macro Causal Effects in C-DMGs—0
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood EstimationCode0
Causal Models for Growing Networks—0
Reinterpreting demand estimation—0
ClusterSC: Advancing Synthetic Control with Donor Selection—0
Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities—0
Body Discovery of Embodied AI—0
Causal Links Between Anthropogenic Emissions and Air Pollution Dynamics in Delhi—0
Differentially Private Joint Independence Test—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based EstimatorsCode0
DeCaFlow: A Deconfounding Causal Generative ModelCode1
World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child—0
KANITE: Kolmogorov-Arnold Networks for ITE estimation—0
Doubly robust identification of treatment effects from multiple environmentsCode0
Causes of evolutionary divergence in prostate cancer—0
Causal Feature Learning in the Social SciencesCode0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling—0
Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation—0
Causal-Ex: Causal Graph-based Micro and Macro Expression Spotting—0
Machine learning algorithms to predict stroke in China based on causal inference of time series analysis—0
A primer on optimal transport for causal inference with observational 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