I asked ChatGPT to:
explain double machine learning for dummies
Here is the answer:
Double machine learning (DML) is a statistical method used to estimate treatment effects in the presence of high-dimensional controls. To put it simply, it's a way to answer questions about cause and effect using data and machine learning, even when there are lots of potentially relevant variables.
Here is a more simplified explanation using a practical example:
Let's say you are a coach of a football team, and you want to determine the impact of a new training method on the performance of your team. However, there are many factors that could affect performance - things like diet, amount of sleep, weather conditions, player mood, previous experience, and more.
With traditional methods, you would have to painstakingly adjust for all these factors, or "controls", to determine the real impact of the new training method. This can be time-consuming, difficult, and may not be accurate, especially if there are a lot of controls.
Here's where double machine learning comes in. DML uses machine learning twice (hence the "double" part) to solve this problem:
First, it uses machine learning to predict the outcome (football performance) based on all the control variables (diet, sleep, weather, etc.), and then subtracts this prediction from the actual outcome. This gives you a "residual" outcome that can't be explained by the controls.
Then, it does the same thing for the treatment (the new training method): it predicts the treatment based on the controls, and subtracts this prediction from the actual treatment. This gives you a "residual" treatment that can't be explained by the controls.
After that, you simply correlate the "residual" outcome with the "residual" treatment to estimate the effect of the treatment. This gives you an estimate of the impact of the new training method, accounting for all the controls without needing to adjust for them one by one.
In other words, double machine learning helps you figure out the cause-and-effect relationship between variables when there are many other variables that could potentially influence the result. It does this by using machine learning to "filter out" the effect of the other variables, leaving just the effect you're interested in.