Summary of Incomescm: From Tabular Data Set to Time-series Simulator and Causal Estimation Benchmark, by Fredrik D. Johansson
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
by Fredrik D. Johansson
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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| 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 proposes a novel approach to creating challenging estimation tasks for evaluating observational estimators of causal effects. By turning observational data into sequential structural causal models (SCM), the authors aim to provide more realistic and complex benchmarks than traditional simulators. The proposed strategy involves fitting real-world data where possible, and composing simple mechanisms to create complexity. The authors implement this approach in a software package called IncomeSCM, which they apply to the Adult income dataset. They then devise multiple estimation tasks and sample datasets to compare established estimators of causal effects. The results show varying quality of effect estimates between methods, highlighting the need for dedicated causal estimators and model selection criteria. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make computers learn from data in a more realistic way. Right now, we often use simple simulations to test how well machine learning models work. But these simulations might not be very similar to real-life situations. To fix this problem, the authors suggest creating complex scenarios by combining small, understandable parts together. They show how this works using a famous dataset called Adult income data set. By doing this, they can compare different methods for predicting what would happen if we did something different in the past. |
Keywords
* Artificial intelligence * Machine learning
