Philipp Bach
@philippbach.bsky.social
Assistant Professor (Juniorprofessor) of Econometrics; FU Berlin; Interests: Causal machine learning, causality, data science, statistics, econometrics ; https://philippbach.github.io/
Thanks, Paul! Looking forward to CDSM
October 27, 2025 at 6:24 PM
Thanks, Paul! Looking forward to CDSM
Oh, here's the handle of Jan 😀: @janteichertkluge.bsky.social
January 6, 2025 at 8:46 AM
Oh, here's the handle of Jan 😀: @janteichertkluge.bsky.social
The paper is joint work with (I guess almost all bsky-less)
Victor Chernozhukov
@svenklaassen.bsky.social
Martin Spindler
Jan Teichert-Kluge
Suhas Vijaykumar
Looking forward to your thoughts, comments and questions!
Victor Chernozhukov
@svenklaassen.bsky.social
Martin Spindler
Jan Teichert-Kluge
Suhas Vijaykumar
Looking forward to your thoughts, comments and questions!
January 6, 2025 at 8:42 AM
The paper is joint work with (I guess almost all bsky-less)
Victor Chernozhukov
@svenklaassen.bsky.social
Martin Spindler
Jan Teichert-Kluge
Suhas Vijaykumar
Looking forward to your thoughts, comments and questions!
Victor Chernozhukov
@svenklaassen.bsky.social
Martin Spindler
Jan Teichert-Kluge
Suhas Vijaykumar
Looking forward to your thoughts, comments and questions!
The causal part:
If you are a #causal #DAG enthusiast, you'll finde some causal diagrams and a discussion on causal aspects of demand analysis in the paper too 😀
#CausalSky #dataSkyence
If you are a #causal #DAG enthusiast, you'll finde some causal diagrams and a discussion on causal aspects of demand analysis in the paper too 😀
#CausalSky #dataSkyence
January 6, 2025 at 8:42 AM
The causal part:
If you are a #causal #DAG enthusiast, you'll finde some causal diagrams and a discussion on causal aspects of demand analysis in the paper too 😀
#CausalSky #dataSkyence
If you are a #causal #DAG enthusiast, you'll finde some causal diagrams and a discussion on causal aspects of demand analysis in the paper too 😀
#CausalSky #dataSkyence
The fun part (that's what you usually don't read in the papers):
Embedding text and image data makes demand analysis pretty accessible from an intuitive point of view. You can play around with the product embeddings, check for similarities and formulate/check hypotheses for various demand patterns
Embedding text and image data makes demand analysis pretty accessible from an intuitive point of view. You can play around with the product embeddings, check for similarities and formulate/check hypotheses for various demand patterns
January 6, 2025 at 8:42 AM
The fun part (that's what you usually don't read in the papers):
Embedding text and image data makes demand analysis pretty accessible from an intuitive point of view. You can play around with the product embeddings, check for similarities and formulate/check hypotheses for various demand patterns
Embedding text and image data makes demand analysis pretty accessible from an intuitive point of view. You can play around with the product embeddings, check for similarities and formulate/check hypotheses for various demand patterns
Our learnings:
We find that text and image data play an important role in predictive and causal demand analysis: Improved demand prediction and advanced heterogeneity analysis using product infos encoded in text and images, e.g., based on similarities and AI/data-driven product categorization.
We find that text and image data play an important role in predictive and causal demand analysis: Improved demand prediction and advanced heterogeneity analysis using product infos encoded in text and images, e.g., based on similarities and AI/data-driven product categorization.
January 6, 2025 at 8:42 AM
Our learnings:
We find that text and image data play an important role in predictive and causal demand analysis: Improved demand prediction and advanced heterogeneity analysis using product infos encoded in text and images, e.g., based on similarities and AI/data-driven product categorization.
We find that text and image data play an important role in predictive and causal demand analysis: Improved demand prediction and advanced heterogeneity analysis using product infos encoded in text and images, e.g., based on similarities and AI/data-driven product categorization.
Our approach:
1️⃣ Enhanced Predictions: AI-driven embeddings significantly improve the accuracy of sales rank and price predictions
2️⃣ Improved Causal Inference: By fine-tuning embeddings for causal tasks, we uncover strong heterogeneity in price elasticity linked to product-specific features
1️⃣ Enhanced Predictions: AI-driven embeddings significantly improve the accuracy of sales rank and price predictions
2️⃣ Improved Causal Inference: By fine-tuning embeddings for causal tasks, we uncover strong heterogeneity in price elasticity linked to product-specific features
January 6, 2025 at 8:42 AM
Our approach:
1️⃣ Enhanced Predictions: AI-driven embeddings significantly improve the accuracy of sales rank and price predictions
2️⃣ Improved Causal Inference: By fine-tuning embeddings for causal tasks, we uncover strong heterogeneity in price elasticity linked to product-specific features
1️⃣ Enhanced Predictions: AI-driven embeddings significantly improve the accuracy of sales rank and price predictions
2️⃣ Improved Causal Inference: By fine-tuning embeddings for causal tasks, we uncover strong heterogeneity in price elasticity linked to product-specific features