Summary of Causal Effect Identification in Lingam Models with Latent Confounders, by Daniele Tramontano and Yaroslav Kivva and Saber Salehkaleybar and Mathias Drton and Negar Kiyavash
Causal Effect Identification in LiNGAM Models with Latent Confounders
by Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 In this paper, researchers explore the concept of generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. They examine two scenarios: when the causal graph is known beforehand and when it’s unknown. The authors provide a complete graphical characterization of identifiable direct or total causal effects among observed variables, as well as efficient algorithms to verify these conditions. Additionally, they propose an adaptation of reconstruction independent component analysis (RICA) for estimating causal effects from observational data given the causal graph. Experimental results demonstrate the effectiveness of this method in estimating causal effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can figure out the cause-and-effect relationships between things when there’s noise and hidden variables involved. The researchers come up with ways to identify these relationships, both when we know what causes what ahead of time and when we don’t. They also develop efficient methods to check if these conditions are met. Finally, they modify an existing algorithm to make it better at figuring out the cause-and-effect relationships from data. |
