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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 401–450 of 1722 papers

TitleStatusHype
Identifying the Effect of PersuasionCode0
Fair Decisions Despite Imperfect PredictionsCode0
A Kernel Test for Causal Association via Noise Contrastive Backdoor AdjustmentCode0
Inference on Optimal Dynamic Policies via Softmax ApproximationCode0
Adversarial Balancing for Causal InferenceCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
Deep representation learning for individualized treatment effect estimation using electronic health recordsCode0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
A Latent Causal Inference Framework for Ordinal VariablesCode0
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLPCode0
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable DataCode0
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational DataCode0
Debiased Bayesian inference for average treatment effectsCode0
DecoR: Deconfounding Time Series with Robust RegressionCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Covariate balancing using the integral probability metric for causal inferenceCode0
χSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid DomainsCode0
Deep Causal Inference for Point-referenced Spatial Data with Continuous TreatmentsCode0
Deriving Causal Order from Single-Variable Interventions: Guarantees & AlgorithmCode0
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time InsightCode0
Learning Causally Predictable Outcomes from Psychiatric Longitudinal DataCode0
Counterfactual Prediction Under Selective ConfoundingCode0
Counterfactually Comparing Abstaining ClassifiersCode0
Counterfactual Mean EmbeddingsCode0
Learning high-dimensional causal effectCode0
Automatic doubly robust inference for linear functionals via calibrated debiased machine learningCode0
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingCode0
Counterfactual FairnessCode0
Learning Representations of Instruments for Partial Identification of Treatment EffectsCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Lifted Causal Inference in Relational DomainsCode0
Causal Cartographer: From Mapping to Reasoning Over Counterfactual WorldsCode0
A Causal Framework for Evaluating Deferring SystemsCode0
Causal Inference with CocyclesCode0
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Logic and Commonsense-Guided Temporal Knowledge Graph CompletionCode0
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language ModelsCode0
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's ContinuumCode0
BART: Bayesian additive regression treesCode0
Convolutional neural networks for valid and efficient causal inferenceCode0
Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component AnalysisCode0
Confounding Feature Acquisition for Causal Effect EstimationCode0
Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match QualityCode0
Neural Network Parameter-optimization of Gaussian pmDAGsCode0
Consistent Estimation of Propensity Score Functions with Oversampled Exposed SubjectsCode0
Conditional Cross-Design Synthesis Estimators for Generalizability in MedicaidCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
Conditional Generative Models are Sufficient to Sample from Any Causal Effect EstimandCode0
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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