The finding is not surprising and it may apply to the use of race/ethnicity in causal mediation analysis.
#causalinference #CausalTwitter #racism #OpenSexism #genderinequity #epitwitter #epiverse ---
RT @yudapearl
Who could ever imagine that causal mediation analysis would be used to unveil gender biases on the US Supreme Court. It did:
file:///C:/Users/Judea/Downloads/text_interruptions%20(2)%20(2).pdf
https://twitter.com/yudapearl/status/1628668226187169794Judea Pearl on Twitter
“Who could ever imagine that causal mediation analysis would be used to unveil gender biases on the US Supreme Court. It did:
file:///C:/Users/Judea/Downloads/text_interruptions%20(2)%20(2).pdf”
🔥Our study (joint with @
[email protected] and @
[email protected]) on the impact of residence permits on the labor market attachment of foreign workers in
#Liechtenstein has been published in the European Economic Review:
https://authors.elsevier.com/a/1gTv%7E3F%7EawXq1 #EconTwitter #CausalTwitter
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.
This latest episode of the Casual Inference podcast on instrumental variables is fantastic and it’s neat to hear about IV from a non-econometrics perspective https://casualinfer.libsyn.com/website/instrumental-variables-with-maria-glymour-season-4-episode-5
and my post on conditional and marginal effects makes a surprise appearance at the beginning lol https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/
#statsodon #causaltwitter
Casual Inference: The Value of Instrumental Variables with Maria Glymour | Season 4 Episode 5
Lucy D'Agostino McGowan and Ellie Murray chat with Maria Glymour, Professor of Epidemiology & Biostatstics at UCSF and incoming chair of the Department of Epidemiology at Boston University. Maria successfully convinces Ellie and Lucy that instrumental variables can be very useful in epidemiology. Follow up: ✍️ Follow along on Twitter: Maria Glymour: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by Quinn Rose:
Today at 4:30pm at the
@MSFTResearch
booth (#202) at #NeurIPS2022, Agrin Hilmkil and Cheng Zhang are demoing our open source Causal AI suite, including our new no-code interface, ShowWhy.
https://microsoft.com/en-us/research/video/introduction-to-showwhy-user-interfaces-for-causal-decision-making/
#causalml #causaltwitter #causalinference
Introduction to ShowWhy, user interfaces for causal decision making - Microsoft Research
ShowWhy is a no-code user interface suite that empowers domain experts to become “decision scientists,” who can independently ask a causal question, develop causal estimates, and present and defend causal evidence to an audience of decision makers. Developed as an open-source application from Microsoft Research, ShowWhy leverages the shared APIs of the DoWhy, EconML, and […]
Elizabeth Silver — Causality and Causal Discovery
Bate-papo suuuuuper legal com o pessoal do @
[email protected]. O @
[email protected] e o @
[email protected]. Acabei de ouvir, e modéstia parte, ficou bem legal!😜
#DataScience #IA #AI #Causalidade #CausalTwitter
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”
Luisa N. Borrell
Martin Huber
Andrew Heiss 
Emre Kıcıman
Marcel Ribeiro-Dantas, PhD🔎📊