Posts tagged counterfactuals
Counterfactual generation using pymc do-operator
- 18 August 2023
In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of counterfactuals in causal analysis using the PyMC framework, with a special focus on the “do-operator.” Counterfactuals are essentially “what-if” scenarios that allow us to understand the potential outcomes had a different action been taken or a different condition been present. By leveraging the PyMC framework and its “do-operator,” we can programmatically simulate these scenarios, giving us a deeper understanding of the relationships between predictors and target variables.
Interrupted time series analysis
- 18 October 2022
This notebook focuses on how to conduct a simple Bayesian interrupted time series analysis. This is useful in quasi-experimental settings where an intervention was applied to all treatment units.
Difference in differences
- 18 September 2022
This notebook provides a brief overview of the difference in differences approach to causal inference, and shows a working example of how to conduct this type of analysis under the Bayesian framework, using PyMC. While the notebooks provides a high level overview of the approach, I recommend consulting two excellent textbooks on causal inference. Both The Effect [Huntington-Klein, 2021] and Causal Inference: The Mixtape [Cunningham, 2021] have chapters devoted to difference in differences.
Counterfactual inference: calculating excess deaths due to COVID-19
- 18 July 2022
Causal reasoning and counterfactual thinking are really interesting but complex topics! Nevertheless, we can make headway into understanding the ideas through relatively simple examples. This notebook focuses on the concepts and the practical implementation of Bayesian causal reasoning using PyMC.
Regression discontinuity design analysis
- 18 April 2022
Quasi experiments involve experimental interventions and quantitative measures. However, quasi-experiments do not involve random assignment of units (e.g. cells, people, companies, schools, states) to test or control groups. This inability to conduct random assignment poses problems when making causal claims as it makes it harder to argue that any difference between a control and test group are because of an intervention and not because of a confounding factor.