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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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 1722 papers

TitleStatusHype
A Primer on Deep Learning for Causal InferenceCode0
Many Proxy Controls—0
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLPCode0
Estimating Potential Outcome Distributions with Collaborating Causal Networks—0
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and ImplicationCode0
Enhancing Model Robustness and Fairness with Causality: A Regularization ApproachCode0
Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs—0
Towards Principled Causal Effect Estimation by Deep Identifiable Models—0
Mutual Information Minimization Based Disentangled Learning Framework For Causal Effect Estimation—0
Connecting Data to Mechanisms with Meta Structual Causal Model—0
Causal Triple Attention Time Series Forecasting—0
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments—0
Automated hypothesis generation via Evolutionary Abduction—0
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting—0
Minimizing Memorization in Meta-learning: A Causal Perspective—0
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap—0
An Automated Approach to Causal Inference in Discrete Settings—0
Conditional Cross-Design Synthesis Estimators for Generalizability in MedicaidCode0
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—0
Optimization-based Causal Estimation from Heterogenous EnvironmentsCode1
Temporal Inference with Finite Factored Sets—0
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Achieving Counterfactual Fairness for Causal Bandit—0
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision—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