A placebo-in-time bootstrap estimates the bias distribution in synthetic control models from the panel to yield trajectory-agnostic confidence intervals calibrated at the zero null.
Debiasing and t-tests for synthetic control infer- ence on average causal effects
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 4representative citing papers
Targeted synthetic control (TSC) is a new two-stage estimator that applies a one-dimensional weight-tilting update to debias synthetic control weights and guarantees the final counterfactual is a convex combination of control outcomes.
Factor-model approach to causal inference in panel data models treatment effects as changes in factor loadings or the factor process itself, without requiring parallel trends.
A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.
citing papers explorer
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Bias-Aware Confidence Intervals for Synthetic Control via Placebo-in-Time Bootstrap
A placebo-in-time bootstrap estimates the bias distribution in synthetic control models from the panel to yield trajectory-agnostic confidence intervals calibrated at the zero null.
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Targeted Synthetic Control Method
Targeted synthetic control (TSC) is a new two-stage estimator that applies a one-dimensional weight-tilting update to debias synthetic control weights and guarantees the final counterfactual is a convex combination of control outcomes.
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Causal Inference Using Factor Models
Factor-model approach to causal inference in panel data models treatment effects as changes in factor loadings or the factor process itself, without requiring parallel trends.
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Piece-wise linear isotonic regression
A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.