Causal inferences need a formal method to quantify sensitivity to causal assumptions. There is no all-purpose algorithm to determine sensitivity. 
This paper aims to find a general method that works for finding the sensitivity of all arbitrary linear SCMs.

University of Michigan - Ann Arbor

Pearl, Judea, et al. “Sensitivity Analysis of Linear Structural Causal Models.”

http://proceedings.mlr.press/v97/cinelli19a/cinelli19a.pdf

Sensitivity Analysis of Linear Structural Causal Models

Keele, L. (2015). The Statistics of Causal Inference: A View from Political Methodology. Political Analysis, 23(3), 313–335. doi: 10.1093/pan/mpv007

The Statistics of Causal Inference: A View from Political Methodology. Political Analysis

Key Idea of Paper

1. Introduce formal sensitivity analysis for linear SCMs and reduce it to a non-zero constrained identification problem.
2. Introduce graphical process PUSH-FORWARD to simplify identification - which can they be solved by a number of algorithms
3. Develop graph-based constrained identification solving algorithm that returns sensitivity curve.

Contributions of Paper

The research on sensitivity analysis of linear structural causal models (SCMs) yielded two primary conclusions:
1. It generated a general framework for conducting sensitivity analyses specifically in linear SCMs.
2. Future research should explore extensions into different models to develop techniques applicable to more general models.

Learn Before

Related