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Found 7 papers, 2 papers with code
Leveraging text data for causal inference using electronic health records
We augment a classic matching analysis by incorporating text in three ways: by using text to supplement a multiple imputation procedure, we improve the fidelity of imputed values to handle missing data; by incorporating text in the matching stage, we strengthen the plausibility of the matching procedure; and by conditioning on text, we can estimate easily interpretable text-based heterogeneous treatment effects that may be stronger than those found across categories of structured covariates.
Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects
We show that exact confidence intervals for the maximum (or minimum) unit-level effect can be obtained by inverting tests for a sequence of bounded nulls.
Methodology Statistics Theory Statistics Theory
Using Simulation to Analyze Interrupted Time Series Designs
We are sometimes forced to use the Interrupted Time Series (ITS) design as an identification strategy for potential policy change, such as when we only have a single treated unit and no comparable controls.
Methodology Applications
Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality
We enhance the precision of these results by developing a predictive model to estimate the match quality of pairs of text documents as a function of our various distance scores.
Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling
In this work, we first show how the standard topic quality measures of coherence and pointwise mutual information act counter-intuitively in the presence of common but irrelevant words, making it difficult to even quantitatively identify situations in which topics may be dominated by stopwords.
Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability
We propose a general framework for topic-specific summarization of large text corpora, and illustrate how it can be used for analysis in two quite different contexts: an OSHA database of fatality and catastrophe reports (to facilitate surveillance for patterns in circumstances leading to injury or death) and legal decisions on workers' compensation claims (to explore relevant case law).
Concise comparative summaries (CCS) of large text corpora with a human experiment
In this paper we propose a general framework for topic-specific summarization of large text corpora and illustrate how it can be used for the analysis of news databases.
