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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 751–800 of 1722 papers

TitleStatusHype
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint—0
Toward a Theory of Causation for Interpreting Neural Code Models—0
A Fast Bootstrap Algorithm for Causal Inference with Large DataCode0
Causal Estimation of Exposure Shifts with Neural NetworksCode0
Causal Confirmation Measures: From Simpson's Paradox to COVID-19—0
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
How to select predictive models for causal inference?—0
A Counterfactual Collaborative Session-based Recommender SystemCode1
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model—0
Integrating Earth Observation Data into Causal Inference: Challenges and OpportunitiesCode1
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization—0
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for MedicineCode1
Convolutional neural networks for valid and efficient causal inferenceCode0
Proximal Causal Learning of Conditional Average Treatment Effects—0
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular DataCode2
Causal Inference under Data Restrictions—0
causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge GraphsCode1
Optimal Transport for Counterfactual Estimation: A Method for Causal InferenceCode0
Non-parametric identifiability and sensitivity analysis of synthetic control models—0
Causal Falsification of Digital TwinsCode0
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial IntelligenceCode1
Interacting Treatments with Endogenous Takeup—0
Subset verification and search algorithms for causal DAGsCode0
Causal Inference for Recommendation: Foundations, Methods and Applications—0
Action needed to make carbon offsets from tropical forest conservation work for climate change mitigation—0
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and GeneralizationCode1
Continual Causal Effect Estimation: Challenges and Opportunities—0
Causal Inference (C-inf) -- closed form worst case typical phase transitions—0
Causal Inference (C-inf) -- asymmetric scenario of typical phase transitions—0
PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation—0
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction—0
Layout-Based Causal Inference for Object Navigation—0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
Causal Deep Learning—0
Identifying causal effects with subjective ordinal outcomes—0
Nuisance Function Tuning and Sample Splitting for Optimal Doubly Robust Estimation—0
Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering—0
Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions—0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems—0
Local Policy Improvement for Recommender Systems—0
Causal Inference for Knowledge Graph based RecommendationCode0
Out-of-sample scoring and automatic selection of causal estimatorsCode2
A Layered Architecture for Universal Causality—0
On the Relationship Between Explanation and Prediction: A Causal View—0
Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution—0
Instrumental Variables in Causal Inference and Machine Learning: A SurveyCode1
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
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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