Adam Smith

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Adam Smith

Adam Smith

@adamnsmith__

Assistant Professor @UCLSoM

London, England Katılım Haziran 2018
477 Takip Edilen397 Takipçiler
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Caio Waisman
Caio Waisman@CaioWaisman·
We have a beautiful literature on experiments with interference between units but what if there’s interference between experimenters? Estimands change and in a way we can understand and unpack. I hope you enjoy this one! 😊
Marketing Science@Mrktng_Science

Articles in Advance 09/24 (4 of 9) "Parallel Experimentation and Competitive Interference on Online Advertising Platforms" by Caio Waisman, Navdeep S. Sahni, Harikesh S. Nair, Xiliang Lin @CaioWaisman @N_sahni pubsonline.informs.org/doi/10.1287/mk…

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Giovanni Compiani
Giovanni Compiani@GioCompiani·
🚨📜CALL FOR PAPERS 📜🚨 Send us your best work using non-standard data (e.g., unstructured data, clickstreams, data from genAI) to learn about consumer preferences. More details below.
Giovanni Compiani@GioCompiani

For people interested in this, we're organizing a conference on related topics (co-chaired with @malika_kor) next spring. The conference will be held at the Chicago Booth School of Business on May 30-31, 2025, and will bring together scholars across fields who use Machine Learning, NLP, and other tools to extract valuable insights from new types of data, including: • unstructured data • clickstream data • data generated by AI. We invite both methodological and empirical submissions. Submission Deadline: January 6, 2025 Submit here (PDF format): ndconference2025.hotcrp.com We’re also thrilled to have @Susan_Athey and @econ_greg as keynote speakers. Stay tuned for more details on registration, and feel free to share this with anyone who might be interested. For more information visit: chicagobooth.edu/research/kilts…

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Daniel Ershov
Daniel Ershov@ershov_daniel·
I’m very happy to share a “new" working paper on the dynamic/learned complementarity that arises between different products. This is joint work with @adamnsmith__ and Max Pachali (@TilburgU) and its been really years in the making! 1/12
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MSI
MSI@MktgScience·
ML/AL-based algorithmic pricing tools soften competition, according to Daniel Ershov of University College London. Catch an MSI #webinar of him discussing his work supporting this side of the debate July 30, 12-12:30: ow.ly/MYis50SIFLg #AI #ML #marketingresearch #marketing
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Jimbo Brand
Jimbo Brand@jamesbrandecon·
I wrote my first blog post, which is me quickly testing the value of combining two approximate demand estimation approaches that I like. Fun to write, but I can tell I have some work to do on blog-style writing. We'll see if there's ever a follow-up! jamesbrandecon.com/blog/0jxkvfr6z…
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Stephan Seiler
Stephan Seiler@SeilerStephan·
🚨🚨 New working paper!!! 🚨🚨 Demand Estimation with Text and Image Data (together with @GioCompiani and Ilya Morozov) papers.ssrn.com/sol3/papers.cf… We propose a method to include product similarity measured using unstructured data into a demand estimation framework. 1/7
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Daniel Ershov
Daniel Ershov@ershov_daniel·
Marketing & Analytics at @UCLSoM is hiring again this year! Come join our fantastic quant group for an awesome research environment, low teaching loads, panoramic views over LDN from Canary Wharf and great vibes! Apply here: apply.interfolio.com/127232
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Jimbo Brand
Jimbo Brand@jamesbrandecon·
Very cool paper showing how Zalando does demand forecasting at scale. This is the second recent paper I've seen describing how companies think about large-scale pricing systems, the other being Amazon's recent paper: assets.amazon.science/ba/f5/f761c2a0… 1/
Armin Kekić@armin_kekic

Glad we can finally share this publicly: new paper on how @ZalandoTech uses deep-learning based forecasting for algorithmic pricing. A rare insight into how the machine learning is used to solve real-world problems. 🧵 1/ 📃: bit.ly/3qrYZXj

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Adam Smith
Adam Smith@adamnsmith__·
@CaioWaisman @eleafeit Caio I think you’re talking about a mixture of normals for the error term, not regression coefficients right? So a mixture for the likelihood? That would be less common than probit w/ mixture of normals heterogeneity.
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Caio Waisman
Caio Waisman@CaioWaisman·
@eleafeit Maybe I’m missing something so I’ll like at it more carefully. Geweke and Keane put them together, but I wanted to see what else was out there.
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Caio Waisman
Caio Waisman@CaioWaisman·
Does anyone know a reference for a Probit model with a mixture of normal distributions specification besides Geweke and Keane (1999)? Ideally with a Bayesian implementation.
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Adam Smith
Adam Smith@adamnsmith__·
@ZhengGong19 I should add: lots of good data sets are also available on Kaggle, UCI machine learning repository, etc. But for teaching, they can be problematic because there are also lots of publicly available analyses and code…
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Zheng Gong
Zheng Gong@ZhengGong19·
Marketing/Econ/B-school folks: I am teaching a data-based marketing analysis course and plan to give students some data for their term mini-paper. I am teaching OLS/logistic reg/demand estimation/cluster analysis/factor analysis/conjoint...etc. Any suitable open datasets?
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Adam Smith
Adam Smith@adamnsmith__·
Happy to have this out at QME! Regularization is important for large models and/or weakly informative data, and researchers stand to gain by thinking carefully about what estimates are shrunk towards. Using domain knowledge may be better than (arbitrary) zeros.
QME Public Editor@QME_Journal

#QME ahead of print: "Shrinkage priors for high-dimensional demand estimation" by @adamnsmith__ and Jim Griffin link.springer.com/article/10.100…

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Dean Eckles
Dean Eckles@deaneckles·
TIL that Poisson misspelled Rev. Thomas Bayes' name throughout his work in the 1830s
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Adam Smith
Adam Smith@adamnsmith__·
@jamesbrandecon @SeilerStephan @Iagg11 Simulations could be useful, especially in exploring/explaining boundaries in performance. And if all candidate models admit elasticity estimates then that could be easy metric. But not always the case with ML. Also depends on whether goal is hh vs market level inference/action.
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Jimbo Brand
Jimbo Brand@jamesbrandecon·
An IO-adjacent paper I would love to read (or write) is one that follows the ML/CS format of taking a complicated simulation/data and horse-racing many demand estimation methods against each other. I think academic work often under-values speed (on multiple dimensions), and 1/
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Daniel Ershov
Daniel Ershov@ershov_daniel·
One of these names is not like the others... 🧐 Today is the last day to register and (among many exciting talks) see me present a brand new (!!??) project on influencing 3rd party design of pricing algorithms (joint w/ @e_lizlyons).
Marion Goussé@MarionGousse

Last week to register to our workshop on "the Impact of AI on Economic Decision" in June 23-24 in Rennes w/ an amazing program, incl. talks by A. Pakes, S. Mullainathan, @m_sendhil @ershov_daniel,@maxkasy, @jannspiess. No fees but need to register at //swll.to/EED22 #EconTwitter

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Shosh Vasserman
Shosh Vasserman@shoshievass·
Twitter friends — I’m collecting datasets that can be used for class projects for viz/regression/prediction (specific proj goal intentionally vague). Can you please send me your fav datasets or links to other ppl’s collections? (Plz be more specific than “Kaggle”) 🙏🙏🙏
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Keşfet

@TilburgU @GioCompiani @UCLSoM @CaioWaisman @eleafeit @ZhengGong19 @jamesbrandecon @SeilerStephan