580 research outputs found
A Bayesian Spatial Propensity Score Matching Evaluation of the Regional Impact of Micro-finance
A Bayesian Spatial-Propensity Score Matching estimator is proposed to measure the regional impact of microfinance on poverty reduction and women's empowerment. The impact of microfinance in Bolivia was tested with this estimator, using census and household survey data. The results suggest that microfinance was useful for poverty reduction and women’s empowerment at municipality level in Bolivia
Latent Causal Socioeconomic Health Index
This research develops a model-based LAtent Causal Socioeconomic Health
(LACSH) index at the national level. We build upon the latent health factor
index (LHFI) approach that has been used to assess the unobservable
ecological/ecosystem health. This framework integratively models the
relationship between metrics, the latent health, and the covariates that drive
the notion of health. In this paper, the LHFI structure is integrated with
spatial modeling and statistical causal modeling, so as to evaluate the impact
of a continuous policy variable (mandatory maternity leave days and
government's expenditure on healthcare, respectively) on a nation's
socioeconomic health, while formally accounting for spatial dependency among
the nations. A novel visualization technique for evaluating covariate balance
is also introduced for the case of a continuous policy (treatment) variable. We
apply our LACSH model to countries around the world using data on various
metrics and potential covariates pertaining to different aspects of societal
health. The approach is structured in a Bayesian hierarchical framework and
results are obtained by Markov chain Monte Carlo techniques.Comment: 31 pages. arXiv admin note: substantial text overlap with
arXiv:1911.0051
Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso
We propose a Bayesian procedure for simultaneous variable and covariance
selection using continuous spike-and-slab priors in multivariate linear
regression models where q possibly correlated responses are regressed onto p
predictors. Rather than relying on a stochastic search through the
high-dimensional model space, we develop an ECM algorithm similar to the EMVS
procedure of Rockova & George (2014) targeting modal estimates of the matrix of
regression coefficients and residual precision matrix. Varying the scale of the
continuous spike densities facilitates dynamic posterior exploration and allows
us to filter out negligible regression coefficients and partial covariances
gradually. Our method is seen to substantially outperform regularization
competitors on simulated data. We demonstrate our method with a re-examination
of data from a recent observational study of the effect of playing high school
football on several later-life cognition, psychological, and socio-economic
outcomes
Generative Causal Inference
In this paper we propose the use of the generative AI methods in
Econometrics. Generative methods avoid the use of densities as done by MCMC.
They directrix simulate large samples of observables and unobservable
(parameters, latent variables) and then using high-dimensional deep learner to
inform a nonlinear transport map from data to parameter inferences. Our themed
apply to a wide verity or econometrics problems, including those where the
latent variables are updates in deterministic fashion. Further, paper we
illustrate our methodology in the field of causal inference and show how
generative AI provides generalization of propensity scores. Our approach can
also handle nonlinearity and heterogeneity. Finally, we conclude with the
directions for future research.Comment: arXiv admin note: text overlap with arXiv:2305.1497
Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients
Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. We study the balancing properties of the estimated propensity scores. Traditional Frequentist propensity scores focus attention on balancing covariates that are strongly associated with treatment. In contrast, we demonstrate that Bayesian propensity scores can be used to balance the association between covariates and the outcome. This balancing property has the effect of reducing confounding bias because it reduces the degree to which covariates are outcome risk factors
- …