Decision trees partition covariate space to detect positivity violations in causal inference, augmented by random forests to quantify violation robustness within each subspace.
Adversarial Bal- ancing for Causal Inference
3 Pith papers cite this work. Polarity classification is still indexing.
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Proposes consistent estimators for average and local treatment effects on treated and respondent users in A/B tests with non-response bias in sentiment surveys, evaluated via simulations.
Proposes causal risk minimization via higher-order moment-balancing error decomposition and attribute projection for high-dimensional treatments, with experiments on continuous, discrete, and text data.
citing papers explorer
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A discriminative approach for finding and characterizing positivity violations using decision trees
Decision trees partition covariate space to detect positivity violations in causal inference, augmented by random forests to quantify violation robustness within each subspace.
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User Sentiment as a Success Metric: Persistent Biases Under Full Randomization
Proposes consistent estimators for average and local treatment effects on treated and respondent users in A/B tests with non-response bias in sentiment surveys, evaluated via simulations.
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Causal Risk Minimization for High-Dimensional Treatments
Proposes causal risk minimization via higher-order moment-balancing error decomposition and attribute projection for high-dimensional treatments, with experiments on continuous, discrete, and text data.