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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 1722 papers

TitleStatusHype
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
Heteroskedasticity as a Signature of Association for Age-Related Genes—0
Hi-CI: Deep Causal Inference in High Dimensions—0
Latent State Inference in a Spatiotemporal Generative Model—0
Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs—0
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation—0
High-dimensional Inference for Dynamic Treatment Effects—0
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework—0
High Precision Causal Model Evaluation with Conditional Randomization—0
Honesty in Causal Forests: When It Helps and When It Hurts—0
How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain—0
How causal inference concepts can guide research into the effects of climate on infectious diseases—0
How to select predictive models for causal inference?—0
How to Understand "Support"? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding—0
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets—0
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study—0
Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference—0
Identifiability of Causal-based Fairness Notions: A State of the Art—0
Identifiability of Gaussian structural equation models with equal error variances—0
Identification and Estimation of Causal Effects from Dependent Data—0
Identification and Estimation of Joint Probabilities of Potential Outcomes in Observational Studies with Covariate Information—0
Identification In Missing Data Models Represented By Directed Acyclic Graphs—0
Identification Methods With Arbitrary Interventional Distributions as Inputs—0
Identification of Average Causal Effects in Confounded Additive Noise Models—0
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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