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 1376–1400 of 1722 papers

TitleStatusHype
Causal Bayesian Optimization—0
Studying Product Competition Using Representation Learning—0
Automatic Detection of Influential Actors in Disinformation NetworksCode1
Principal Fairness for Human and Algorithmic Decision-Making—0
Causal Modeling of Twitter Activity During COVID-19Code1
On the power of conditional independence testing under model-XCode0
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation—0
Multivariate Time Series Forecasting with Transfer Entropy GraphCode1
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates—0
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles—0
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
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects—0
Machine learning for causal inference: on the use of cross-fit estimatorsCode0
Causal Inference under Outcome-Based Sampling with Monotonicity AssumptionsCode0
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
Ivy: Instrumental Variable Synthesis for Causal Inference—0
A category theoretical argument for causal inference—0
Causal Relational Learning—0
Causal Inference of Script Knowledge—0
Identification Methods With Arbitrary Interventional Distributions as Inputs—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
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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