Bayesian Causality

Tags:

• bayes
• causal-inference
• causal-model
• causality

Bayesian sensitivity analysis for causal effects from 2 tables in the presence of unmeasured confounding with application to presidential campaign visits

Tags:

• bayesian-inference
• bayes
• confounding
• sensitivity-analysis
• causal-inference

The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores

Tags:

• bayes
• causal-inference
• propensity
• causality

Bayesian propensity score analysis for observational data

Tags:

• confounding
• observational-study
• propensity-score
• bias
• causal-inference
• bayesian-statistics

Notes:

• using Bayesian credible intervals to adjust for uncertainty in estimation of propensity score;relied heavily on Rubin 5-category propensity adjustment

When should epidemiologic regressions use random coefficients?

Tags:

• bayesian-methods
• shrinkage
• causal-inference
• empirical-bayes-estimators
• epidemiologic-method
• hierarchical-regression
• mixed-models
• multilevel-modeling
• random-coefficient-regression
• variance-components

Notes:

• use of statistics in epidemiology is largely primitive;stepwise variable selection on confounders leaves important confounders uncontrolled;composition matrix;example with far too many significant predictors with many regression coefficients absurdly inflated when overfit;lack of evidence for dietary effects mediated through constituents;shrinkage instead of variable selection;larger effect on confidence interval width than on point estimates with variable selection;uncertainty about variance of random effects is just uncertainty about prior opinion;estimation of variance is pointless;instead the analysis should be repeated using different values;"if one feels compelled to estimate $\tau^{2}$, I would recommend giving it a proper prior concentrated amount contextually reasonable values";claim about ordinary MLE being unbiased is misleading because it assumes the model is correct and is the only model entertained;shrinkage towards compositional model;"models need to be complex to capture uncertainty about the relations...an honest uncertainty assessment requires parameters for all effects that we know may be present. This advice is implicit in an antiparsimony principle often attributed to L. J. Savage 'All models should be as big as an elephant (see Draper, 1995)'". See also gus06per.