To control in a data-driven way for potentially high dimensional pre-treatment covariates that motivate the selection-on-observables assumptions, we adapt the double machine learning framework to sample selection problems. That is, we make use of (a) Neyman-orthogonal and doubly robust score ...
It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consists of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be ...
Notably, the double machine learning (DML) model proposed by Chernozhukov et al. has garnered widespread attention [2]. Within the framework of a partially linear model, DML allows for the estimation of the average treatment effect. The estimation process can be decomposed into two stages: in ...
Notably, the double machine learning (DML) model proposed by Chernozhukov et al. has garnered widespread attention [2]. Within the framework of a partially linear model, DML allows for the estimation of the average treatment effect. The estimation process can be decomposed into two stages: in ...
Double / debiased machine learning framework ofChernozhukov et al. (2018)for Partially linear regression models (PLR) Partially linear IV regression models (PLIV) Interactive regression models (IRM) Interactive IV regression models (IIVM) The object-oriented implementation ofDoubleMLthat is based on...
Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptio
Evaluating the machine learning framework’s performance at these distances (Fig. 6), the single-camera model closely matches the telecentric dataset as the camera moves farther from the object. For each distance, the tuned pipeline returns differing segment sizes and overlaps, which are also ...
ddml: Double/debiased machine learning in Stata Achim Ahrens (ETH Zürich) Mark E Schaffer (Heriot-Watt University, IZA) Christian B Hansen (University of Chicago) Thomas Wiemann (University of Chicago) Package website: https://statalasso.github.io/ Latest version available here November 18, ...
In this post I’m going to talk about a generalization of the double selection for any Machine Learning (ML) method described by Chernozhukov et al. (2016). Suppose you are in the following framework: where is the parameter of interest, is a set of control variables and and are error ...
We estimate the conditional expectations E [yi |xi , di = 0] and E [yi |xi , di = 1] as well as E [di |xi ] using a supervised machine learner. 9 / 25 DDML models The DDML framework can be applied to other models (all implemented in ddml): Partial linear IV model yi = ...