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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1151–1160 of 1722 papers

Title	Date	Tasks	Status	Hype
Exposing Disparities in Flood Adaptation for Equitable Future Interventions	Dec 6, 2023	Causal Inference	—Unverified	0
Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks	Sep 21, 2023	Causal InferenceGraph Attention	—Unverified	0
FAIR: A Causal Framework for Accurately Inferring Judgments Reversals	Jun 20, 2023	Causal Inference	—Unverified	0
fairadapt: Causal Reasoning for Fair Data Pre-processing	Oct 19, 2021	AttributeBIG-bench Machine Learning	—Unverified	0
Fair Effect Attribution in Parallel Online Experiments	Oct 15, 2022	AttributeCausal Inference	—Unverified	0
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion	May 25, 2017	Causal DiscoveryCausal Inference	—Unverified	0
FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training	Oct 6, 2022	AttributeCausal Inference	—Unverified	0
Fast Restricted Causal Inference	Jul 13, 2017	Causal Inference	—Unverified	0
Feature Selection as Causal Inference: Experiments with Text Classification	Aug 1, 2017	Causal InferenceClassification	—Unverified	0
A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data	Nov 27, 2021	Causal Inferencefeature selection	—Unverified	0

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All datasets IHDP Jobs IDHP

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Random Forest	Average Treatment Effect Error	0.96	—	Unverified
2	Balancing Linear Regression	Average Treatment Effect Error	0.93	—	Unverified
3	k-NN	Average Treatment Effect Error	0.79	—	Unverified
4	CEVAE	Average Treatment Effect Error	0.46	—	Unverified
5	Balancing Neural Network	Average Treatment Effect Error	0.42	—	Unverified
6	Causal Forest	Average Treatment Effect Error	0.4	—	Unverified
7	BCAUS DR	Average Treatment Effect Error	0.29	—	Unverified
8	TARNet	Average Treatment Effect Error	0.28	—	Unverified
9	Counterfactual Regression + WASS	Average Treatment Effect Error	0.27	—	Unverified
10	MTDL-KNN	Average Treatment Effect Error	0.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CFR WASS	Average Treatment Effect on the Treated Error	0.09	—	Unverified
2	CFR MMD	Average Treatment Effect on the Treated Error	0.08	—	Unverified
3	BART	Average Treatment Effect on the Treated Error	0.08	—	Unverified
4	GANITE	Average Treatment Effect on the Treated Error	0.06	—	Unverified
5	BCAUSS	Average Treatment Effect on the Treated Error	0.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BART	Average Treatment Effect Error	0.34	—	Unverified
2	OLS with separate regressors for each treatment	Average Treatment Effect Error	0.31	—	Unverified
3		Average Treatment Effect Error	-0.23	—	Unverified