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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 1426–1450 of 1722 papers

TitleStatusHype
Causal inference in network experiments: regression-based analysis and design-based properties—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
Causal Inference in Observational Data—0
Causal Inference in the Presence of Interference in Sponsored Search Advertising—0
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts—0
Causal Inference Isn't Special: Why It's Just Another Prediction Problem—0
Causal Inference of Script Knowledge—0
Causal Inference on Discrete Data via Estimating Distance Correlations—0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning—0
Causal Inference on Multivariate and Mixed-Type Data—0
Causal Inference on Outcomes Learned from Text—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
Causal Inference out of Control: Estimating the Steerability of Consumption—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Causal Inference Struggles with Agency on Online Platforms—0
Causal Inference through a Witness Protection Program—0
Causal inference through multi-stage learning and doubly robust deep neural networks—0
Estimation and Inference on Nonlinear and Heterogeneous Effects—0
Causal Inference Tools for a Better Evaluation of Machine Learning—0
Causal Inference Under Approximate Neighborhood Interference—0
Causal Inference under Data Restrictions—0
Causal Inference Under Interference And Network Uncertainty—0
Causal Inference under Interference through Designed Markets—0
Causal Inference under Networked Interference and Intervention Policy Enhancement—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