Type | Journal Article |
---|---|
Author | Pierre Baldi |
Author | Babak Shahbaba |
URL | https://doi.org/10.1080/00031305.2019.1647876 |
Volume | 0 |
Issue | 0 |
Pages | 1-9 |
Publication | The American Statistician |
ISSN | 0003-1305 |
Date | August 12, 2019 |
DOI | 10.1080/00031305.2019.1647876 |
Accessed | 8/30/2019, 11:14:58 AM |
Library Catalog | Taylor and Francis+NEJM |
Abstract | Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes. |
Date Added | 8/30/2019, 11:14:58 AM |
Modified | 8/30/2019, 11:15:43 AM |
Type | Journal Article |
---|---|
Author | Luke Keele |
Author | Kevin M. Quinn |
URL | https://doi.org/10.1214/17-AOAS1048 |
Volume | 11 |
Issue | 4 |
Pages | 1974-1997 |
Publication | Ann App Stat |
Date | 2017-12 |
Extra | Citation Key: kee17bay tex.citeulike-article-id= 14546623 tex.citeulike-linkout-0= http://dx.doi.org/10.1214/17-AOAS1048 tex.citeulike-linkout-1= https://doi.org/10.1214/17-AOAS1048 tex.posted-at= 2018-03-09 20:04:17 tex.priority= 2 |
DOI | 10.1214/17-AOAS1048 |
Date Added | 7/7/2018, 1:38:33 PM |
Modified | 11/8/2019, 8:01:59 AM |
Type | Journal Article |
---|---|
Author | Corwin Matthew Zigler |
URL | https://doi.org/10.1080/00031305.2015.1111260 |
Volume | 70 |
Issue | 1 |
Pages | 47-54 |
Publication | The American Statistician |
ISSN | 0003-1305 |
Date | January 2, 2016 |
Extra | PMID: 27482121 |
DOI | 10.1080/00031305.2015.1111260 |
Accessed | 11/25/2019, 7:36:13 AM |
Library Catalog | Taylor and Francis+NEJM |
Abstract | Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes’ theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of Bayes’ theorem, which presupposes a full probability model for the observed data that adheres to the likelihood principle. The goal of this article is to explicate this fundamental feature of Bayesian estimation of causal effects with propensity scores to provide context for the existing literature and for future work on this important topic.[Received June 2014. Revised September 2015.] |
Date Added | 11/25/2019, 7:36:13 AM |
Modified | 11/25/2019, 7:37:12 AM |
Type | Journal Article |
---|---|
Author | Lawrence C. McCandless |
Author | Paul Gustafson |
Author | Peter C. Austin |
Volume | 28 |
Pages | 94-112 |
Publication | Stat Med |
Date | 2009 |
Extra | Citation Key: mcc09bay tex.citeulike-article-id= 13265723 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0 |
Date Added | 7/7/2018, 1:38:33 PM |
Modified | 11/8/2019, 8:01:59 AM |
using Bayesian credible intervals to adjust for uncertainty in estimation of propensity score;relied heavily on Rubin 5-category propensity adjustment
Type | Journal Article |
---|---|
Author | Sander Greenland |
URL | http://dx.doi.org/10.1111/j.0006-341X.2000.00915.x |
Volume | 56 |
Pages | 915-921 |
Publication | Biometrics |
Date | 2000 |
Extra | Citation Key: gre00whe tex.citeulike-article-id= 13265446 tex.citeulike-linkout-0= http://dx.doi.org/10.1111/j.0006-341X.2000.00915.x tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0 |
DOI | 10.1111/j.0006-341X.2000.00915.x |
Date Added | 7/7/2018, 1:38:33 PM |
Modified | 11/8/2019, 8:01:59 AM |
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.