Predictive State Propensity Subclassification (PSPS): A causal inference method for optimal data-driven propensity score stratification
Abstract
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.
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.
Research Areas
