8 Advanced tools with the DoubleML library
This chapter covers
- Estimating the impact of a continuous decision variable with Double Machine Learning
- Learning to calculate confidence intervals for the ATE
- Combining two estimators to get a better one with doubly robust techniques
In the previous chapters, we covered the basic theory and practice of how to remove the effect of confounders using the adjustment formula. In this chapter, we will go one step further, and explore three separate techniques that will improve the accuracy and reliability of our causal estimates. These techniques, double machine learning, confidence intervals, and doubly robust techniques, are used when precision is paramount. For instance, people working in healthcare or finance domains might put these approaches to work.
Even though learning these techniques takes a while, usem them is straightforward with open-source libraries. Fortunately, the methods discussed in this chapter have already been implemented in the Double ML library, available for both R and Python. After delving into the theoretical foundations, we will explore how to effectively employ these methods using this library.
Be aware that you don’t need to read this chapter to be able to follow the rest of the book. So, if these topics are not a priority, or they are too technical, you can always skip this chapter, and come back later when you need to learn these tools.