Robust Causal Inference using Double/Debiased Machine Learning

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

"Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised ...

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 ...

Implementation of the Double/ Debiased Machine Learning ...

As proposed by Chernozhukov's Double/Debiased Machine Learning (DML) framework, we will estimate the causal effects of binary treatments on an ...

The value added of machine learning to causal inference: evidence ...

We focus on the double machine learning, causal forest, and generic machine learning methods, in the context of both average and heterogeneous ...

Double/Debiased Machine Learning for Dynamic Treatment Effects

We consider evaluating the causal effects of dynamic treatments, i.e. of multiple treatment sequences in various periods, based on double ...

Double Machine Learning Density Estimation for Local Treatment ...

Double debiased machine learning nonparametric inference with. 356 ... Robust causal inference with continuous instruments. 402 using the local ...

Orthogonal/Double Machine Learning - EconML

Our package offers several variants for the final model estimation. Many of these variants also provide valid inference (confidence interval construction) for ...

On Doubly Robust Inference for Double Machine Learning in ...

The combination of doubly robust methods with flexible, data-adaptive estimation of nuisance parameters thus underpins recent developments in targeted learning ...

Double Robust, Flexible Adjustment Methods for Causal Inference

Methods covered include Augmented Inverse Propensity Weighting, Targeted Maximum Likelihood Estimation, and Double/Debiased Machine Learning. Results suggest ...

An Object-Oriented Implementation of Double Machine Learning in R

It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods ...

[PDF] Machine learning in the estimation of causal effects: targeted ...

This commentary discusses issues around the use of data-adaptive regression in estimation of causal inference parameters and focuses on two estimation ...

Chernozhukov et al. on Double / Debiased Machine Learning - CDN

√n-consistent! Causal Inference with ML using Orthogonalization. To overcome ... robust to small perturbations in η0. It doesn't change much when η0 ...

Causal Machine Learning with Doubly Robust Estimation and ...

With the growing prominence of data science, causal machine learning has become appealing for inferring treatment effects in observational studies, surpassing ...

Double Debiased Machine Learning (part 2) - Towards Data Science

The idea is the same behind post-double selection: reduce the regularization bias by performing variable selection twice. The estimator is still ...

Double Robust, Flexible Adjustment Methods for Causal Inference

Methods covered include Augmented Inverse Probability Weighting (AIPW), Targeted Maximum. Likelihood Estimation (TMLE), and Double or Debiased Machine Learning ...

Causal Inference 0.4cm Chapter 4.2 Treatment Effect Heterogeneity ...

– a special case of double/debiased machine learning. 60 / 66. Page 61. Another double approach: TMLE. ▷ Targeted Maximum Likelihood Estimation or Targeted ...

Causal Inference with Machine Learning August 25, 2022 - AWS

In: Journal of Econometrics 34. (1987), pp. 305–334 (cited on page 15). [10] Victor Chernozhukov et al. 'Double/debiased machine learning for ...

DoubleML: Double Machine Learning in R

Program Evaluation and. Causal Inference With High-Dimensional Data. ... (2021), Multiway Cluster Robust Double/Debiased. Machine Learning ...

A Tutorial on Doubly Robust Learning for Causal Inference

Doubly robust learning offers a robust framework for causal inference from observational data by integrating propensity score and outcome ...

Interflex with DML Estimators - Yiqing Xu

This section explores the ideas behind the Double/Debiased Machine Learning (DML), highlighting its use of modern machine learning to robustly estimate marginal ...