Summary of On the Effects Of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement, by Ahmad Saeed Khan et al.
On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement
by Ahmad Saeed Khan, Erik Schaffernicht, Johannes Andreas Stork
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the long-standing issue of estimating treatment effects from observational data in healthcare, education, and economics. Current deep disentanglement-based methods to address selection bias fall short due to insufficient handling of irrelevant variables, leading to prediction errors. The authors propose a novel approach that disentangles pre-treatment variables using a deep embedding method, explicitly identifies and represents irrelevant variables, and introduces a reconstruction objective. They create an embedding space for irrelevant variables using an attached autoencoder and employ orthogonalization to prevent irrelevant information from leaking into the latent space representations of other factors. Experimental results on synthetic and real-world benchmark datasets demonstrate that this approach can better identify irrelevant variables and more precisely predict treatment effects than previous methods, with less degradation in prediction quality when additional irrelevant variables are introduced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in healthcare and economics. When we try to figure out how something works after it was changed (like a medicine or a policy), we often have to deal with extra information that’s not important. Right now, the best methods for getting rid of this extra information are pretty bad at doing their job. The authors came up with a new way to handle this problem by using deep learning and special math tricks. They showed that this new approach can do better than old approaches at guessing how something would work if we didn’t have all the extra information. |
Keywords
» Artificial intelligence » Autoencoder » Deep learning » Embedding » Embedding space » Latent space
