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 1401–1425 of 1722 papers

TitleStatusHype
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time SeriesCode0
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks—0
Causal Modeling with Stochastic Confounders—0
Machine learning for causal inference: on the use of cross-fit estimatorsCode0
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects—0
Causal Inference under Outcome-Based Sampling with Monotonicity AssumptionsCode0
Ivy: Instrumental Variable Synthesis for Causal Inference—0
A category theoretical argument for causal inference—0
Causal Relational Learning—0
Identification Methods With Arbitrary Interventional Distributions as Inputs—0
Causal Inference of Script Knowledge—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—0
ParKCa: Causal Inference with Partially Known CausesCode0
Interpretable Personalization via Policy Learning with Linear Decision Boundaries—0
Targeting customers under response-dependent costsCode0
Towards Clarifying the Theory of the Deconfounder—0
Deconfounded Image Captioning: A Causal Retrospect—0
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference—0
Causal Inference With Selectively Deconfounded Data—0
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable ModelsCode0
Causal Inference under Networked Interference and Intervention Policy Enhancement—0
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness—0
On Geometry of Information Flow for Causal Inference—0
A Survey on Causal InferenceCode0
Show:102550
← PrevPage 57 of 69Next →
All datasetsIHDPJobsIDHP

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