Robust Causal Inference using Double/Debiased Machine Learning

2024-09-18 | Input Talk | Achim Ahrens Abstract Motivated by their robustness to partially unknown functional forms, supervised machine ...

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

I have heard about using double/debiased machine learning for causal inference (Chernozhukov, et al 2016), and even played around with it as ...

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

Double/Debiased machine learning can be used to recover causal effects even if relationships between confounders and the treatment, and between confounders and ...

Double debiased machine learning nonparametric inference with ...

Using doubly robust influence function and cross-fitting, we give tractable primitive conditions under which the nuisance estimators do not affect the first- ...

Double/Debiased Machine Learning for Economists: Practical ...

The advent of Double/Debiased Machine Learning (DML), an innovative framework for causal inference using ML, developed by Chernozhukov et al.

Double Debiased Machine Learning Nonparametric Inference with ...

We propose a doubly robust inference method for causal effects of continuous treatment vari- ables, under unconfoundedness and with ...

Double/debiased Machine Learning for Causal Inference on ...

Through simulations, we demonstrate the double robustness property of our method and its improved performance, compared to existing estimators ...

[Q]what do you think about the machine learning based methods for ...

Something like causal machine learning is not a substitute for DAGs although sometimes they might be advertised as such. For double machine ...

Using double-debiased machine learning to estimate the impact of ...

Machine learning approaches provide an alternative approach to traditional fixed effects estimators in causal inference. In particular ...

Double/Debiased Machine Learning for Treatment and Structural ...

partially linear regression model and for using DML to estimate and do inference ... Targeted Learning: Causal Inference for Obser- vational and Experimental Data ...

Double/debiased machine learning for treatment and structural ...

... inference for parameters within the partially linear model using lasso‐type methods without cross‐fitting. By relying upon cross‐fitting, the DML approach ...

Double/Debiased Machine Learning for Causal Inference on ...

Through simulations, we demonstrate the double robustness property of our method and its improved performance, compared to existing estimators ...

Anytime-Valid Inference for Double/Debiased Machine Learning of ...

Double (debiased) machine learning (DML) has seen widespread use in recent years for learning causal/structural parameters, in part due to its flexibility and ...

Double/debiased Machine Learning for Causal Inference on ...

This paper discusses the use of double/debiased machine learning (DML) for es- ... In the field of causal inference, doubly robust (DR) estimators have been ...

Chernozhukov et al. on Double / Debiased Machine Learning

We typically condition on confounders by making strong assumptions about the ... Causal Inference with ML using Orthogonalization. To overcome this ...

Robust double machine learning model with application to omics data

Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), ...

Double debiased machine learning nonparametric inference with ...

2Our estimator is doubly robust in the sense that the causal effect remains identified and consistently estimated if either one of the nuisance functions E ...

22 - Debiased/Orthogonal Machine Learning - Matheus Facure

The nice thing about Double-ML is that it frees us from all the hassle of learning the nuisance parameters in a causal model. With that, we can focus all our ...

MZES Methods Bites on X: " Content alert New recording and ...

Robust Causal Inference using Double/Debiased Machine Learning: A... 2024-09-18 | Input Talk | Achim AhrensAbstractMotivated by their robustness ...

ddml: Double/Debiased Machine Learning in Stata

st0001, causal inference, machine learning, doubly-robust estimation ... Program Evalu- ation and Causal Inference With High-Dimensional Data.