Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence

Knaus, Michael C.; Lechner, Michael; Strittmatter, Anthony

doi:10.1093/ectj/utaa014

Cited by 85 publications

(109 citation statements)

References 55 publications

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“…A small but growing strand of literature puts causal machine learning estimators for heterogeneous treatment effects into practice and discusses practically relevant issues (e.g. Bertrand, Crépon, Marguerie, & Premand, 2017; Davis & Heller, 2017; Knaus, Lechner, & Strittmatter, 2020a, 2020b). However, applications that do the same for average treatment effects are currently missing.…”

Section: Introductionmentioning

confidence: 99%

A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills

Knaus

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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This study investigates the dose–response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning parameter choices in the machine learning part? (ii) How to assess covariate balancing in high‐dimensional settings? The results show that improvements in objectively measured cognitive skills require at least medium intensity, while improvements in school grades are already observed for low intensity of practice.

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Section: Introductionmentioning

confidence: 99%

A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills

Knaus

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Among the recent methods developed in the causal machine learning literature, causal forest have gained relevance [ 95 – 97 ]. Knaus et al [ 98 ] show that causal forests perform consistently well across different data generation processes and aggregation levels. The causal forest algorithm [ 96 ] is a forest-based method for treatment effect estimation that allows for a tractable asymptotic theory and valid statistical inference extending Breiman’s random forest algorithm.…”

Section: • Artificial Neural Network: Extreme Learning Machinementioning

confidence: 99%

A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests

2020

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Understanding the digital jump of bank customers is key to design strategies to bring on board and keep online users, as well as to explain the increasing competition from new providers of financial services (such as BigTech and FinTech). This paper employs a machine learning approach to examine the digitalization process of bank customers using a comprehensive consumer finance survey. By employing a set of algorithms (random forests, conditional inference trees and causal forests) this paper identities the features predicting bank customers’ digitalization process, illustrates the sequence of consumers’ decision-making actions and explores the existence of causal relationships in the digitalization process. Random forests are found to provide the highest performance–they accurately predict 88.41% of bank customers’ online banking adoption and usage decisions. We find that the adoption of digital banking services begins with information-based services (e.g., checking account balance), conditional on the awareness of the range of online services by customers, and then is followed by transactional services (e.g., online/mobile money transfer). The diversification of the use of online channels is explained by the consciousness about the range of services available and the safety perception. A certain degree of complementarity between bank and non-bank digital channels is also found. The treatment effect estimations of the causal forest algorithms confirm causality of the identified explanatory factors. These results suggest that banks should address the digital transformation of their customers by segmenting them according to their revealed preferences and offering them personalized digital services. Additionally, policymakers should promote financial digitalization, designing policies oriented towards making consumers aware of the range of online services available.

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“…Recently, this literature has seen a surge of proposed methods, in particular in epidemiology and econometrics. Knaus, Lechner, and Strittmatter (2018) compare many of those methods systematically with respect to their set-up as well as their performance in a simulation exercise. One conclusion from their paper is that random forest-based estimation approaches outperform alternative estimators.…”

Section: Estimationmentioning

confidence: 99%

Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

Cockx¹,

Lechner²,

Bollens³

2020

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Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence

Cited by 85 publications

References 55 publications

A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills

A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills

A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests

Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

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