Extension usage examples:
Here's how our browser extension sees the article:
The Use of Machine Learning in Treatment Effect Estimation | SpringerLink
Source: link.springer.com
Article summary:
1. This chapter presents recent developments in the econometrics literature showing that machine learning methods can be used to estimate treatment effects from observational data.
2. The double machine learning (DML) approach is used to select relevant control variables and functional forms for consistent estimation of average treatment effect.
3. Another strand of the literature focuses on treatment effect heterogeneity through the discovery of the conditional average treatment effect (CATE) function, which can be estimated by either projecting it on a pre-specified coordinate or estimating the entire function.
Article analysis:
The article is generally reliable and trustworthy, as it provides an overview of recent developments in the econometrics literature regarding the use of machine learning for treatment effect estimation from observational data. The authors provide a clear explanation of how the double machine learning (DML) approach works, as well as how it can be used to select relevant control variables and functional forms for consistent estimation of average treatment effect. Additionally, they discuss another strand of the literature which focuses on treatment effect heterogeneity through the discovery of the conditional average treatment effect (CATE) function, and explain how this can be estimated by either projecting it on a pre-specified coordinate or estimating the entire function.
The article does not appear to have any biases or one-sided reporting, as it provides an objective overview of current research in this field without making any unsupported claims or omitting any points of consideration. Furthermore, there is no promotional content or partiality present in the article, and all possible risks are noted where applicable. The article also presents both sides equally by providing an unbiased overview of current research in this field without favouring one side over another.