Jing Kong
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We introduce Predictive State Propensity Subclassification (PSPS), a
novel estimation method for undertaking causal inference from
observational studies. The methodology applies to both discrete and
continuous treatments and can estimate unit-level and population-level
average treatment effects. PSPS combines propensity and outcome models
into one encompassing probabilistic model, which can be jointly
estimated using maximum likelihood or Bayesian inference. We give a
detailed overview on the TensorFlow implementation for likelihood
optimization and show via large-scale simulations that it outperforms
several state of the art methods -- both in terms of bias and variance
for average as well as unit-level treatment effects. Finally we
illustrate the methodology and algorithms on standard datasets in the
literature.
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Uncovering common themes from a large number of unorganized search queries is a primary step to mine insights about aggregated user interests. Common topic modeling techniques for document modeling often face sparsity problems with search query data as these are much shorter than documents. We present two novel techniques that can discover semantically meaningful topics in search queries: i) word co-occurrence clustering generates topics from words frequently occurring together; ii) weighted bigraph clustering uses URLs from Google search results to induce query similarity and generate topics. We exemplify our proposed methods on a set of Lipton brand as well as make-up & cosmetics queries. A comparison to standard LDA clustering demonstrates the usefulness and improved performance of the two proposed methods.
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