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

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Showing 1276–1300 of 1722 papers

TitleStatusHype
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron ResolutionCode1
On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering—0
Reconsidering Generative Objectives For Counterfactual ReasoningCode1
General Transportability of Soft Interventions: Completeness Results—0
Learning Causal Effects via Weighted Empirical Risk Minimization—0
RealCause: Realistic Causal Inference Benchmarking—0
Causal inference using deep neural networks—0
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder—0
Invariant Representation Learning for Treatment Effect EstimationCode1
Rethinking recidivism through a causal lensCode0
A scoping review of causal methods enabling predictions under hypothetical interventions—0
Confounding Feature Acquisition for Causal Effect EstimationCode0
Incorporating Causal Effects into Deep Learning Predictions on EHR Data—0
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions—0
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same EndCode2
DoWhy: An End-to-End Library for Causal InferenceCode3
Adversarial Counterfactual Learning and Evaluation for Recommender SystemCode1
Causal Expectation-MaximisationCode0
Causal Autoregressive FlowsCode1
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks—0
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation—0
Instrumental Variable Identification of Dynamic Variance DecompositionsCode1
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Exploring Logically Dependent Multi-task Learning with Causal Inference—0
Causal Inference for Spatial TreatmentsCode1
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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