Summary of Network Causal Effect Estimation in Graphical Models Of Contagion and Latent Confounding, by Yufeng Wu et al.
Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding
by Yufeng Wu, Rohit Bhattacharya
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel approach is introduced to investigate whether observed correlations between units in networks are primarily caused by contagion or latent confounding, utilizing a segregated graph representation of these mechanisms. The study examines how uncertainty about the true underlying mechanism affects downstream computation of network causal effects under full interference scenarios, where each unit may depend on any other unit. Under certain assumptions, likelihood ratio tests are derived to identify dependence due to contagion or latent confounding in sets of variables across units. Network causal effect estimation strategies are proposed that provide unbiased and consistent estimates if the dependence mechanisms are known or correctly inferred using these tests. The methods allow for network effect estimation in a wider range of full interference scenarios not considered previously. The effectiveness of the methods is evaluated with synthetic data, and the validity of assumptions is tested using real-world networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A research paper investigates how to tell if correlations between things in a network are caused by one thing spreading to another or because they have something in common that’s not showing up yet. They use a special way of looking at these situations called segregated graphs. The researchers want to know how being unsure about which is happening affects what we can learn from the data. They come up with tests and methods to figure out what’s going on and provide accurate answers. This helps us understand networks better, especially when things are connected in complicated ways. |
Keywords
» Artificial intelligence » Likelihood » Synthetic data
