Quantifying Intrinsic Causal Contributions via Structure Preserving Interventions

Core Concepts

The authors propose a method to quantify intrinsic causal contributions by separating the information added by each node from its ancestors using structure-preserving interventions. This approach provides insights into the 'intrinsic' part of causal influence.

Abstract

The content discusses quantifying intrinsic causal contributions through structure-preserving interventions in DAGs. It introduces the notion of 'intrinsic' contribution and explains how it differs from inherited parts. The method is applied to real-world examples like river flows and fuel consumption data, showcasing its practical implications.

Key points include:

• Proposal of a method to quantify intrinsic causal contributions.
• Explanation of the distinction between 'intrinsic' and 'inherited' parts of causal influence.
• Application of the method to datasets on river flows and fuel consumption.
• Comparison with existing measures of causal influence.
• Consideration of computational complexity and SCM learning in the context of ICC.

Stats

The low values for New Jumbles Rock and Samlesbury show that the flow at the root notes already explains most of the variation of the former.
For the AUTO MPG dataset, ICCShVar values after normalizing with total variance are: cyl 64%, dis 17%, hp < 1%, wgt 3%, mpg 15%.

Quotes

"In this case, a measure of causal influence that quantifies the variance reduction by usual do-interventions would be the right one to assess the impact of these dams."
"The intermediate nodes like dis, hp, and wgt mostly inherit uncertainty from their parents, but roughly 1/7 of the variance of mpg remains unexplained by all factors."

Key Insights Distilled From

Quantifying intrinsic causal contributions via structure preserving interventions

by Domi... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2007.00714.pdf

Deeper Inquiries

Can ICC be generalized to handle more complex models with cycles and latent variables

ICC can be generalized to handle more complex models with cycles and latent variables by allowing for dependent noise terms. In cases where there are common causes of nodes or when the model includes cycles, ICC can still be applied by defining the worth of a coalition for different subsets based on interventional probabilities. This approach considers adjustments of mechanisms downstream while randomizing those of the ancestors, ensuring that the intrinsic contribution is captured even in more intricate causal structures.

How does ICC compare to other measures when considering confounded scenarios

When considering confounded scenarios, ICC stands out compared to other measures in its ability to separate the information added at a specific node from what is inherited from its predecessors. While some measures may struggle with distinguishing between these two aspects, ICC excels at quantifying the 'intrinsic' contribution of each node on a target variable. By adjusting mechanisms rather than values and accounting for all possible orderings in a DAG, ICC provides a robust measure of causal influence even in complex and confounded scenarios.

What are potential implications or limitations when applying ICC in practical scenarios beyond controlled experiments

Applying ICC in practical scenarios beyond controlled experiments may have implications and limitations that need to be considered. One potential implication is that ICC offers insights into understanding causal contributions within real-world systems where multiple factors interact causally. However, limitations may arise due to computational complexity when exact computation of Shapley values is required. Additionally, inferring structural causal models (SCMs) from observational data can pose challenges if strong restrictions on model classes are not imposed. Despite these considerations, applying ICC can provide valuable insights into attributing contributions within complex systems and guiding decision-making processes based on causal influences identified through this method.