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 351–375 of 1722 papers

TitleStatusHype
LLMs Are Prone to Fallacies in Causal Inference—0
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs—0
Spillover Detection for Donor Selection in Synthetic Control Models—0
Standardizing Structural Causal ModelsCode0
Causal Post-Processing of Predictive Models—0
Investigating potential causes of Sepsis with Bayesian network structure learning—0
Orthogonalized Estimation of Difference of Q-functions—0
Counterfactual-based Root Cause Analysis for Dynamical Systems—0
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark FrameworkCode1
Test-Time Fairness and Robustness in Large Language Models—0
DecoR: Deconfounding Time Series with Robust RegressionCode0
Heterogeneous Treatment Effects in Panel DataCode0
G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes—0
Learning Divergence Fields for Shift-Robust Graph RepresentationsCode1
Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios—0
Reconciling Heterogeneous Effects in Causal Inference—0
Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencodersCode0
A Tutorial on Doubly Robust Learning for Causal Inference—0
Causal Contrastive Learning for Counterfactual Regression Over Time—0
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
A Causal Framework for Evaluating Deferring SystemsCode0
Synthetic Potential Outcomes and Causal Mixture Identifiability—0
SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic GraphsCode0
Is machine learning good or bad for the natural sciences?Code0
Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts—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