DeepAI AI Chat
Log In Sign Up

A spectral adjustment for spatial confounding

12/22/2020
by   Yawen Guan, et al.
0

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessary conditions on the coherence between the treatment variable of interest and the unmeasured confounder that ensure the causal effect of the treatment is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. The key assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the treatment variable to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matern coherence function to more robust semi-parametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets

READ FULL TEXT

page 8

page 25

page 39

09/24/2019

Selecting a Scale for Spatial Confounding Adjustment

Unmeasured, spatially-structured factors can confound associations betwe...
11/06/2020

Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend

Unmeasured confounding is a key threat to reliable causal inference base...
03/01/2022

On Testability of the Front-Door Model via Verma Constraints

The front-door criterion can be used to identify and compute causal effe...
12/06/2021

A stableness of resistance model for nonresponse adjustment with callback data

The survey world is rife with nonresponse and in many situations the mis...
03/05/2017

Controlling for Unobserved Confounds in Classification Using Correlational Constraints

As statistical classifiers become integrated into real-world application...
05/02/2021

Synthesized Difference in Differences

Randomized clinical trials (RCTs) eliminate confounding but impose stric...