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

Showing 14511500 of 1722 papers

TitleStatusHype
ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing FlowsCode0
Double Robust Representation Learning for Counterfactual PredictionCode0
Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying OverlapCode0
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models?Code0
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival AnalysisCode0
Time-uniform central limit theory and asymptotic confidence sequencesCode0
Doubly robust identification of treatment effects from multiple environmentsCode0
Doubly Robust Inference on Causal Derivative Effects for Continuous TreatmentsCode0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Moment-Matching Graph-Networks for Causal InferenceCode0
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured ConfoundingCode0
Choice Set Confounding in Discrete ChoiceCode0
Challenges of Using Text Classifiers for Causal InferenceCode0
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data AssimilationCode0
RNN-based counterfactual prediction, with an application to homestead policy and public schoolingCode0
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door AdjustmentCode0
Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal MatchingCode0
Causal-StoNet: Causal Inference for High-Dimensional Complex DataCode0
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects EstimationCode0
Targeted VAE: Variational and Targeted Learning for Causal InferenceCode0
Learning Causally Predictable Outcomes from Psychiatric Longitudinal DataCode0
Dynamic Structural Impact of the COVID-19 Outbreak on the Stock Market and the Exchange Rate: A Cross-country Analysis Among BRICS NationsCode0
Causal Effect Identification in lvLiNGAM from Higher-Order CumulantsCode0
Optimal Transport for Counterfactual Estimation: A Method for Causal InferenceCode0
Optimal transport weights for causal inferenceCode0
Optimising Individual-Treatment-Effect Using BanditsCode0
Assessing External Validity Over Worst-case SubpopulationsCode0
On the power of conditional independence testing under model-XCode0
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and ImplicationCode0
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation dataCode0
Combining Interventional and Observational Data Using Causal ReductionsCode0
Learning Conditional Instrumental Variable Representation for Causal Effect EstimationCode0
Robust detection and attribution of climate change under interventionsCode0
Using representation balancing to learn conditional-average dose responses from clustered dataCode0
Targeting customers under response-dependent costsCode0
Causal affect prediction model using a facial image sequenceCode0
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual InferenceCode0
Causal Expectation-MaximisationCode0
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?Code0
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary NoiseCode0
Whittemore: An embedded domain specific language for causal programmingCode0
Causal Estimation of Exposure Shifts with Neural NetworksCode0
Learning high-dimensional causal effectCode0
Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in MobilityCode0
Can Transformers Do Enumerative Geometry?Code0
Learning Individual Causal Effects from Networked Observational DataCode0
Orthogonal Machine Learning: Power and LimitationsCode0
ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction ApplicationsCode0
Enhancing Model Robustness and Fairness with Causality: A Regularization ApproachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93Unverified
3k-NNAverage Treatment Effect Error0.79Unverified
4CEVAEAverage Treatment Effect Error0.46Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42Unverified
6Causal ForestAverage Treatment Effect Error0.4Unverified
7BCAUS DRAverage Treatment Effect Error0.29Unverified
8TARNetAverage Treatment Effect Error0.28Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27Unverified
10MTDL-KNNAverage Treatment Effect Error0.23Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08Unverified
3BARTAverage Treatment Effect on the Treated Error0.08Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31Unverified
3Average Treatment Effect Error-0.23Unverified