Bias Analysis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Causal Effect Inference with Deep Latent-Variable Models (2017, neutral): “Consider for example the causal graphs in Figure 1: it’s well known [20, 15, 18, 31, 41] that it is often incorrect to treat the proxies X as if they are ordinary confounders, as this would induce bias.”
On Measurement Bias in Causal Inference (2010, methodology): “It is motivated by a result known to epidemiologists (Greenland and Kleinbaum, 1983; Greenland and Lash, 2008) and regression analysts (Carroll et al.”
Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources (2009, background): “There is now a vast literature on models to fulfill this need, sometimes described under the general heading of bias analysis (Greenland, 2003a, 2005a, 2009; Greenland and Lash, 2008).”
Sensitivity Analysis in Observational Research: Introducing the E-Value (2017, methodology): “However, information about the distribution of the unmeasured confounder, when available, may be helpful in sensitivity analysis, and techniques are available to use such information (21, 25, 29).”
Good practices for quantitative bias analysis. (2014, methodology): “Histograms that depict the frequency of adjusted estimates from the iterations of a probabilistic bias analysis can be used to compare bias analysis results with the conventional results, the results of various bias models with one another, and the progression of results across the sequence of model”