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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 351–400 of 1722 papers

TitleStatusHype
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
Causal Discovery using Compression-Complexity MeasuresCode0
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patientsCode0
Detecting and Measuring Confounding Using Causal Mechanism ShiftsCode0
On Causal Inference with Model-Based OutcomesCode0
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects EstimationCode0
A Brief Review of Hypernetworks in Deep LearningCode0
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and ImplicationCode0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
A Practical Approach to Causal Inference over TimeCode0
Causal Discovery in Linear Structural Causal Models with Deterministic RelationsCode0
Approaching an unknown communication system by latent space exploration and causal inferenceCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
How Fragile is Relation Extraction under Entity Replacements?Code0
Environment Invariant Linear Least SquaresCode0
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging DatasetsCode0
DecoR: Deconfounding Time Series with Robust RegressionCode0
Estimating heterogeneous survival treatment effect in observational data using machine learningCode0
Causal discovery for observational sciences using supervised machine learningCode0
Causal Inference for Knowledge Graph based RecommendationCode0
Adversarial Balancing for Causal InferenceCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Debiased Bayesian inference for average treatment effectsCode0
Deep Causal Inference for Point-referenced Spatial Data with Continuous TreatmentsCode0
Covariate balancing using the integral probability metric for causal inferenceCode0
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLPCode0
Explaining Text Classifiers with Counterfactual RepresentationsCode0
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable DataCode0
Causal Inference from Text: Unveiling Interactions between VariablesCode0
Causal Inference under Outcome-Based Sampling with Monotonicity AssumptionsCode0
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational DataCode0
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingCode0
Counterfactual Prediction Under Selective ConfoundingCode0
Counterfactually Comparing Abstaining ClassifiersCode0
Counterfactual Mean EmbeddingsCode0
Variable Importance Matching for Causal InferenceCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Generalized Encouragement-Based Instrumental Variables for Counterfactual RegressionCode0
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time InsightCode0
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal InferenceCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Convolutional neural networks for valid and efficient causal inferenceCode0
GST-UNet: Spatiotemporal Causal Inference with Time-Varying ConfoundersCode0
Causal Inference in Possibly Nonlinear Factor ModelsCode0
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's ContinuumCode0
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language ModelsCode0
Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approachCode0
Heterogeneous Peer Effects in the Linear Threshold ModelCode0
Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in ChildrenCode0
Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component AnalysisCode0
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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