A Python toolbox for Bayesian Nonparametric Quasi-Experimental Design.
The de facto standard for causal inference is the randomized controlled trial, where one compares a manipulated group with a control group in order to determine the effect of an intervention. However, this research design is not always realistically possible due to pragmatic or ethical concerns. In these situations, quasi-experimental designs (QEDs may provide a solution, as these allow for causal conclusions at the cost of additional design assumptions.
In this repository, we provide the implementation of a Bayesian non-parametric model-comparison-based take on QED, called BNQD. It quantifies (the presence of) an effect using a Bayes factor and and Bayesian model averaged posterior distribution. For basic usage, see the demo.py script, and for slightly more intricate examples, see the empirical examples and simulations.
The current implementation of BNQD depends on GPy.
- Max Hinne, Marcel van Gerven and Luca Ambrogioni, 2019. Causal inference using Bayesian non-parametric quasi-experimental design. ArXiv: https://arxiv.org/abs/1911.06722.