Martin HuberJust under a month left until my book #CausalAnalysis - #ImpactEvaluation and #CausalMachineLearning with Applications in R is released on Aug 1st by @mitpress: https://mitpress.mit.edu/9780262545914/causal-analysis/
🔥 Very happy to see our study (joint with Henrika Langen) on the application of #CausalMachineLearning in quantitative #marketing / #BusinessAnalytics published in #PLOSONE: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278937 #CausalTwitter #DataScience
How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across different subgroups of customers, e.g., between clients with relatively high vs. low prior purchases. Finally, we use optimal policy learning to determine (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention’s effectiveness in terms of sales. We find that only two out of the five coupon categories examined, namely coupons applicable to the product categories of drugstore items and other food, have a statistically significant positive effect on retailer sales. The assessment of group average treatment effects reveals substantial differences in the impact of coupon provision across customer groups, particularly across customer groups as defined by prior purchases at the store, with drugstore coupons being particularly effective among customers with high prior purchases and other food coupons among customers with low prior purchases. Our study provides a use case for the application of causal machine learning in business analytics to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.
Exciting news concerning my textbook "#CausalAnalysis: #ImpactEvaluation and #CausalMachineLearning with Applications in R (#RStats). I've just submitted the final corrections to the publisher @[email protected]
😊The book will be available in summer 2023, including a free online version.
😊The book will be available in summer 2023, including a free online version.
Join the talk of Jannis Kueck (Uni Hamburg) at the virtual Causal Data Science Meeting #cdsm22 this afternoon, who will present our joint paper on "Testing the identification of causal effects in observational data": https://www.causalscience.org/meeting/programme/programme-2022/ #DataScience #CausalMachineLearning
Marcel Ribeiro-Dantas, PhD🔎📊Delighted to announce that @[email protected] is going to publish my book "#CausalAnalysis: Impact evaluation and #CausalMachineLearning with applications in #RStats". 😃 #EconTwitter #EpiTwitter #CausalTwitter
Special thanks to @[email protected] and @[email protected] for their support and advice! https://twitter.com/CausalHuber/status/1463235219167662084
🐦🔗: https://twitter.com/CausalHuber/status/1513415481046323200
MartinHuber on Twitter
“#EconTwitter check out the 1st draft of my book #CausalAnalysis, covering methods of #CausalInference/#PolicyEvaluation (also trends like #CausalMachineLearning) and including practical examples in R (#RStats) - here's the link: https://t.co/b0EpXziovL #EpiTwitter #CausalTwitter”