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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 1651–1675 of 1722 papers

TitleStatusHype
Causal Inference in Observational Data—0
Entropic Causal InferenceCode0
Sensitivity Maps of the Hilbert-Schmidt Independence Criterion—0
Limits to causal inference with state-space reconstruction for infectious disease—0
ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data—0
The Inflation Technique for Causal Inference with Latent Variables—0
Identifying Causal Relations Using Parallel Wikipedia Articles—0
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining—0
From Dependence to Causation—0
Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-Recidivism Policies in Colombia—0
Ancestral Causal InferenceCode0
The Crossover Process: Learnability and Data Protection from Inference Attacks—0
Causal Bandits: Learning Good Interventions via Causal Inference—0
Learning Representations for Counterfactual InferenceCode0
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging dataCode0
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index—0
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies—0
Causal inference for data-driven debugging and decision making in cloud computing—0
Recommendations as Treatments: Debiasing Learning and Evaluation—0
Private Causal Inference—0
Causal and anti-causal learning in pattern recognition for neuroimaging—0
MERLiN: Mixture Effect Recovery in Linear NetworksCode0
Emoticons vs. Emojis on Twitter: A Causal Inference Approach—0
Causal Model Analysis using Collider v-structure with Negative Percentage Mapping—0
Markov Boundary Discovery with Ridge Regularized Linear 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