Estimating Causal Effects with Double Machine Learning

[2403.14385] Estimating Causal Effects with Double Machine Learning

Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various ...

Estimating Causal Effects with Double Machine Learning - arXiv

In this paper, we review one of the most prominent methods - “double/debiased machine learning” (DML) - and empirically evaluate it by comparing its ...

Estimating Identifiable Causal Effects through Double Machine ...

In recent years, there is an explosion in the use of modern. Page 2. machine learning (ML) methods to account for very com- plex and high-dimensional nuisance ...

Estimating Identifiable Causal Effects through Double Machine ...

Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging ...

Estimating Causal Effects with Double Machine Learning

Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear ...

Double Machine Learning for Causal Inference: A Practical Guide

Unlike traditional predictive modeling, which focuses on forecasting outcomes, causal ML is concerned with estimating the causal effect of a ...

[D] Double machine learning, econometrics and causal inference

In stark contrast to classical literature, Chernozhukov leverages machine learning methods to enhance the estimation of causal effects. His ...

Estimating Identifiable Causal Effects through Double Machine ...

Double/Debiased Machine Learning (DML). DML meth- ods (Chernozhukov et al. 2018) are based on two ideas: (1). Use a Neyman orthogonal score4 to ...

Estimating Identifiable Causal Effects through Double Machine ...

○ Standard Plug-in estimator for the general causal functional estimates suffer from same problems. Page 6. Double/Debiased Machine Learning (DML) Estimator.

Double Machine Learning for causal inference | by Borja Velasco

A general framework for estimating causal effects using Machine Learning techniques · Machine Learning is, from the point of view of statistics, a collection of ...

Double Machine Learning in Data Science : r/datascience - Reddit

Failing to capture the true functional form can result in bias in causal effect estimates. Hence, one would be interested in finding a way to ...

Lab 2: Estimating causal effects using double machine learning

We are trying to find the best predictor of Y given X, assuming that E[Y|X] has a linear form. It just so happens that (under some assumptions) we can call the ...

Estimating Identifiable Causal Effects on Markov Equivalence Class ...

We then construct a double/debiased machine learning (DML) estimator that is robust to model misspecification and biases in nuisance function estimation, ...

Double Machine Learning; Beyond Predictive Modeling - causaLens

To do this, solutions need to be able to understand causal effects, so that interventional ('what-if') and counterfactual questions can be asked of the model ...

Orthogonal/Double Machine Learning - EconML

Then the method combines these two predictive models in a final stage estimation so as to create a model of the heterogeneous treatment effect. The approach ...

Causal Inference with high-dimensional data: An evaluation of the ...

In my last blog post, I demonstrated that double machine learning can be used effectively to estimate causal effects in the presence of ...

DoubleMLDeep: Estimation of Causal Effects with Multimodal Data

... causal inference and treatment effect estimation. We propose a neural network architecture that is adapted to the double machine learning (DML) framework ...

Jin Tian: Estimating Identifiable Causal Effects through ... - YouTube

Jin Tian (Iowa State University): Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven ...

Vector Research Blog: Causal Effect Estimation Using Machine ...

The concept of double/debiased machine learning or Double ML (DML) also known as R-learner4 has emerged as a method to achieve an unbiased ...

Causal hybrid modeling with double machine learning - arxiv-sanity

The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks ...