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 1001–1025 of 1722 papers

TitleStatusHype
Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts—0
Sparse Nested Markov models with Log-linear Parameters—0
Spectral Representation Learning for Conditional Moment Models—0
Spillover Detection for Donor Selection in Synthetic Control Models—0
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions—0
SPORTSCausal: Spill-Over Time Series Causal Inference—0
Stability of Linear Structural Equation Models of Causal Inference—0
Stabilized Inverse Probability Weighting via Isotonic Calibration—0
Beauty Learning and Counterfactual Inference—0
Statistical Agnostic Regression: a machine learning method to validate regression models—0
STGCN-LSTM for Olympic Medal Prediction: Dynamic Power Modeling and Causal Policy Optimization—0
STOAT: Spatial-Temporal Probabilistic Causal Inference Network—0
Stochastic Intervention for Causal Effect Estimation—0
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Stochastic Tree Ensembles for Estimating Heterogeneous Effects—0
Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit—0
Strategic Classification is Causal Modeling in Disguise—0
Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift—0
String Diagrams with Factorized Densities—0
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU—0
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation—0
Structure Causal Models and LLMs Integration in Medical Visual Question Answering—0
Structured Neural Networks for Density Estimation and Causal Inference—0
Studying Product Competition Using Representation Learning—0
Super learning in the SAS system—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