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 BNQD demo.ipynb notebook.
The current implementation of BNQD was tested using the following dependencies:
- GPflow 2.0.0
- tensorflow 2.1.0
- tensorboard 2.1.1
- tensorflow_probability 0.9
- ptable 0.9.2
- Max Hinne, Marcel van Gerven and Luca Ambrogioni, 2020. Causal inference using Bayesian non-parametric quasi-experimental design. ArXiv: https://arxiv.org/abs/1911.06722.