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 201225 of 1722 papers

TitleStatusHype
A Multi-level Neural Network for Implicit Causality Detection in Web TextsCode0
Deriving Causal Order from Single-Variable Interventions: Guarantees & AlgorithmCode0
Dirac Delta Regression: Conditional Density Estimation with Clinical TrialsCode0
Deep Counterfactual Networks with Propensity-DropoutCode0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
DecoR: Deconfounding Time Series with Robust RegressionCode0
Adapting Neural Networks for the Estimation of Treatment EffectsCode0
Deep Causal Inference for Point-referenced Spatial Data with Continuous TreatmentsCode0
A Primer on Deep Learning for Causal InferenceCode0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
An adaptive denoising recommendation algorithm for causal separation biasCode0
Causal Effect Identification in lvLiNGAM from Higher-Order CumulantsCode0
Debiased Bayesian inference for average treatment effectsCode0
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Interpretable Almost Matching Exactly for Causal InferenceCode0
BART: Bayesian additive regression treesCode0
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingCode0
Covariate balancing using the integral probability metric for causal inferenceCode0
Counterfactually Comparing Abstaining ClassifiersCode0
A Causal Framework for Evaluating Deferring SystemsCode0
Counterfactual Mean EmbeddingsCode0
Counterfactual Prediction Under Selective ConfoundingCode0
Automatic doubly robust inference for linear functionals via calibrated debiased machine learningCode0
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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