Double Machine Learning for Causal Inference

Double Machine Learning for causal inference | by Borja Velasco

This post tries to explain, briefly yet comprenhensively enough, what Double Machine Learning is and how it works.

Double Machine Learning for Causal Inference: A Practical Guide

Double Machine Learning (DML), also known as de-biased machine learning, was first proposed by MIT statistician and economist Victor Chernozhukov.

Orthogonal/Double Machine Learning - EconML

For fully non-parametric heterogeneous treatment effect models, check out the NonParamDML and the CausalForestDML . For more options of non-parametric CATE ...

[D] Double machine learning, econometrics and causal inference

His double machine learning framework, for example, is very straightforward and yet powerfully capable of analyzing data in the realm of High-dimensional data.

22 - Debiased/Orthogonal Machine Learning - Matheus Facure

... causal inference literature. The paper was called Double Machine Learning for Treatment and Causal Parameters and it took a lot of people to write it ...

1. The basics of double/debiased machine learning

Regularization bias in simple ML-approaches#. Naive inference that is based on a direct application of machine learning methods to estimate the causal parameter ...

Is double machine learning doubly robust? If so, how?

Is double/debiased machine learning doubly robust to endogeneity? I have heard about using double/debiased machine learning for causal inference ...

Double Machine Learning, Clearly Explained (Part 1) - YouTube

In this video, I try to clearly explain about double machine learning technique, used commonly in causal inference. This is the first part ...

Estimating Causal Effects with Double Machine Learning - arXiv

... causal inference frameworks based on machine learning. Traditionally, (supervised) machine learning (ML) has established itself as a ...

Double Machine Learning, Simplified: Part 1 — Basic Causal ...

The conceptual & practical distinctions between statistical/machine learning (ML) and causal inference/econometric (CI) tasks have been established for years— ...

Double Machine Learning; Beyond Predictive Modeling - causaLens

Double Machine Learning (DoubleML) provides a framework to estimate unbiased relationships between variables. It works by enabling the model to disentangle ...

Learning Resource: Causal Machine Learning with DoubleML

In summary, double machine learning in causal inference provides a robust and efficient way to estimate causal effects in high-dimensional ...

Double Machine Learning - An Easy Introduction | Dean Markwick

Double machine learning is an attempt to understand the effect a treatment has on a response without being unduly influenced by the covariates.

Double Machine Learning for Causal Inference from a Partially ...

Double Machine Learning for Causal Inference from a Partially Linear Model · Double/Debiased machine learning can be used to recover causal ...

Double Machine Learning at Scale to Predict Causal Impact of ...

We outline the DML methodology and implemen- tation. We show examples of average treatment effect and conditional average treatment effect (i.e., customer-level) ...

Tutorial on DoubleML for double machine learning in Python and R

Philipp Bach and Sven Klaassen: Tutorial on DoubleML for double machine learning in Python and R · Online Causal Inference Seminar · Susan Athey ...

[2403.14385] Estimating Causal Effects with Double Machine Learning

This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we ...

DoubleML Trainings: Trainings in Causal Machine Learning with ...

What is Double Machine Learning? ... Double Machine Learning is a general approach to Causal Machine Learning. In short, all ML algorithms introduce some form of ...

Machine learning for causal inference: on the use of cross-fit ...

... machine learning algorithms used to estimate nuisance functions. Cross-fit estimators share similarities with double machine learning, cross ...

Double machine learning and automated confounder selection

Double machine learning (DML) has become an ... machine learning algorithms for automated variable selection in a causal inference setting.