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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 1722 papers

TitleStatusHype
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional CovarianceCode0
Finding Valid Adjustments under Non-ignorability with Minimal DAG KnowledgeCode0
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge BasesCode0
Do large language models and humans have similar behaviors in causal inference with script knowledge?Code0
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional DistributionsCode0
Do-calculus enables estimation of causal effects in partially observed biomolecular pathwaysCode0
Automated causal inference in application to randomized controlled clinical trialsCode0
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time SeriesCode0
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic ControlCode0
Double Machine Learning for Causal Inference under Shared-State InterferenceCode0
Moment-Matching Graph-Networks for Causal InferenceCode0
A Latent Causal Inference Framework for Ordinal VariablesCode0
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved ConfoundersCode0
Dirac Delta Regression: Conditional Density Estimation with Clinical TrialsCode0
A Kernel Test for Causal Association via Noise Contrastive Backdoor AdjustmentCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Diffusion Model in Causal Inference with Unmeasured ConfoundersCode0
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal InferenceCode0
Discovering Ancestral Instrumental Variables for Causal Inference from Observational DataCode0
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric EstimationCode0
Detecting and Measuring Confounding Using Causal Mechanism ShiftsCode0
Deep representation learning for individualized treatment effect estimation using electronic health recordsCode0
Deriving Causal Order from Single-Variable Interventions: Guarantees & AlgorithmCode0
Detecting clinician implicit biases in diagnoses using proximal causal inferenceCode0
A Primer on Deep Learning for Causal InferenceCode0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
On the power of conditional independence testing under model-XCode0
Deep Counterfactual Networks with Propensity-DropoutCode0
Detecting hidden confounding in observational data using multiple environmentsCode0
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial NetsCode0
A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal ReasoningCode0
Debiased Bayesian inference for average treatment effectsCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
Covariate balancing using the integral probability metric for causal inferenceCode0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
A Survey on Causal InferenceCode0
Counterfactual Prediction Under Selective ConfoundingCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingCode0
Counterfactual Mean EmbeddingsCode0
A Brief Review of Hypernetworks in Deep LearningCode0
Counterfactual FairnessCode0
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
Counterfactually Comparing Abstaining ClassifiersCode0
DecoR: Deconfounding Time Series with Robust RegressionCode0
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal DataCode0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender SystemsCode0
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language ModelsCode0
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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