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 501–525 of 1722 papers

TitleStatusHype
Selective machine learning of doubly robust functionals—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation—0
Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets—0
Causal programming: inference with structural causal models as finding instances of a relation—0
Causal Discovery with Stage Variables for Health Time Series—0
Causal Q-Aggregation for CATE Model Selection—0
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks—0
Causal Reasoning in Software Quality Assurance: A Systematic Review—0
Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis—0
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study—0
Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects—0
Comment on "Blessings of Multiple Causes"—0
Compositional Models for Estimating Causal Effects—0
Computational Causal Inference—0
Conformal Counterfactual Inference under Hidden Confounding—0
Conterfactual Generative Zero-Shot Semantic Segmentation—0
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset—0
Estimating a Directed Tree for Extremes—0
Collaborative causal inference on distributed data—0
Causal Effect Identification in Uncertain Causal Networks—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Causal Discovery in Dynamic Fading Wireless Networks—0
Causal Discovery from Conditionally Stationary Time Series—0
Applications of statistical causal inference in software engineering—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