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 1351–1400 of 1722 papers

TitleStatusHype
Covariate balancing using the integral probability metric for causal inferenceCode0
Normalizing Flows for Interventional Density EstimationCode0
Causal Feature Learning in the Social SciencesCode0
Causal fault localisation in dataflow systemsCode0
Counterfactual FairnessCode0
Twin Papers: A Simple Framework of Causal Inference for Citations via CouplingCode0
Integer Programming for Causal Structure Learning in the Presence of Latent VariablesCode0
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under OcclusionsCode0
Causal Falsification of Digital TwinsCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component AnalysisCode0
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language ModelsCode0
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine LearningCode0
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative FilteringCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Debiased Bayesian inference for average treatment effectsCode0
Subset verification and search algorithms for causal DAGsCode0
CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health stateCode0
Automatic doubly robust inference for linear functionals via calibrated debiased machine learningCode0
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional DataCode0
On Adaptive Propensity Score Truncation in Causal InferenceCode0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
Convolutional neural networks for valid and efficient causal inferenceCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
One-Step Estimation of Differentiable Hilbert-Valued ParametersCode0
Unbiased Recommender Learning from Missing-Not-At-Random Implicit FeedbackCode0
Automated causal inference in application to randomized controlled clinical trialsCode0
Surrogate Outcomes and TransportabilityCode0
DecoR: Deconfounding Time Series with Robust RegressionCode0
Deep Causal Inference for Point-referenced Spatial Data with Continuous TreatmentsCode0
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic ModelsCode0
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's ContinuumCode0
Deep Counterfactual Networks with Propensity-DropoutCode0
Interventional Video Grounding with Dual Contrastive LearningCode0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
Intervention Design for Effective Sim2Real TransferCode0
SurvCaus : Representation Balancing for Survival Causal InferenceCode0
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden ConfoundersCode0
A Primer on Deep Learning for Causal InferenceCode0
Causal Effect Estimation using Variational Information BottleneckCode0
Causal Effect Estimation on Hierarchical Spatial Graph DataCode0
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
Consistent Estimation of Propensity Score Functions with Oversampled Exposed SubjectsCode0
Deep representation learning for individualized treatment effect estimation using electronic health recordsCode0
Causal Discovery using Compression-Complexity MeasuresCode0
Causal Discovery in Linear Structural Causal Models with Deterministic RelationsCode0
Deriving Causal Order from Single-Variable Interventions: Guarantees & AlgorithmCode0
Confounding Feature Acquisition for Causal Effect EstimationCode0
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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