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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 1301–1325 of 1722 papers

TitleStatusHype
Learning Causal State Representations of Partially Observable Environments—0
Learning Causal Structures Using Regression Invariance—0
Learning Conditional Average Treatment Effects in Regression Discontinuity Designs using Bayesian Additive Regression Trees—0
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects—0
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization—0
Learning Fair Policies for Multi-stage Selection Problems from Observational Data—0
Learning from Pairwise Marginal Independencies—0
Learning high-dimensional directed acyclic graphs with latent and selection variables—0
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data—0
Learning Information Propagation in the Dynamical Systems via Information Bottleneck Hierarchy—0
Learning Invariant Causal Mechanism from Vision-Language Models—0
Learning linear structural equation models in polynomial time and sample complexity—0
Learning Optimal Fair Policies—0
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS)—0
Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery—0
Learning Structural Causal Models from Ordering: Identifiable Flow Models—0
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects—0
Learning Weighted Representations for Generalization Across Designs—0
Lee Bounds with a Continuous Treatment in Sample Selection—0
Leveraging Causal Inference for Explainable Automatic Program Repair—0
Leveraging directed causal discovery to detect latent common causes—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
Limits of Approximating the Median Treatment Effect—0
Limits to causal inference with state-space reconstruction for infectious disease—0
Linear and nonlinear causality in financial markets—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