Understanding the causal effect of variables in systems or processes is important. However, it can be challenging to get started with Bayesian models, as there are multiple libraries with their own strengths and weaknesses. In this blog, we will compare six popular causal inference libraries in their functionality, ease of use, and flexibility. All libraries are compared using hands-on examples for the same data set and in the same context. The included libraries are Bnlearn, Pgmpy, CausalNex, DoWhy, Pyagrum, and CausalImpact. By the end of this blog, you will have a better understanding of these six causal inference libraries, and allow you to select the causal library that best fits your use case.
If you found this article helpful, you are welcome to follow me because I write more about data science! I recommend experimenting with the hands-on examples in this blog. This will help you to learn quicker, understand better, and remember longer. Grab a coffee and have fun! Disclaimer: I am the author of the BNlearn Library for Python, but I’ve kept these comparisons objective and unbiased.
A Brief Introduction to Bayesian Modeling.
Causal inference is the process of determining the cause-and-effect relationships between variables in a process or system. In general, we can separate variables into two distinct groups: driver and passenger variables. Driver variables are those that directly influence the outcome or dependent variable, while passenger variables are those that do not have a direct effect but are correlated/ associated with the outcome variable.
The identification of driver variables can be crucial in projects such as predictive maintenance or in the security domain. The driver variables can help explain the causal relationship between the predictor and outcome variables. In contradiction, passenger variables do not have a direct effect on the outcome variable. However, they can still be useful as they can provide additional variation and thus an understanding of the context in which the data was collected.
For example, if we find that engine failures are strongly correlated with location, we might suspect that there is an underlying driver variable that is causing the failure for a specific location.
Causal inference helps to make better decisions by identifying which variables to manipulate and which variables to monitor. More details about Bayesian modeling can be found in this blog.
The Starters Guide to Causal Structure Learning with Bayesian Methods in Python.
In causal inference, we move beyond prediction to identify the mechanisms and pathways through which events arise.
Overall, performing causal inference analysis is challenging because it requires separating the effects of multiple variables, accounting for confounding variables, and dealing with uncertainty. Luckily, Python has several libraries that can help data scientists perform causal inference. In this article, we will go through six of the most popular causal inference libraries in Python: Bnlearn, Pgmpy, DoWhy, CausalNex, Pyagrum, and CausalImpact.
