Causal inference for social network data

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic result is the first to allow for dependence of each observation on a growing number of other units as sample size increases. While previous methods have generally implicitly focused on one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties, and for dependence due to latent similarities among nodes sharing ties. We describe estimation and inference for new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. Using our methods to reanalyze the Framingham Heart Study data used in one of the most influential and controversial causal analyses of social network data, we find that after accounting for network structure there is no evidence for the causal effects claimed in the original paper

Robust inference in poverty mapping

Small area estimation (SAE) methods are widely used for estimating poverty indicators at finer levels of a country’s geography. Three unit-level SAE techniques – the ELL method (Elbers, Lanjouw, and Lanjouw, 2003), also known as the World Bank method, the Empirical Best Prediction (EBP) method (Molina and Rao, 2010) and the M-Quantile (MQ) method (Tzavidis et al., 2008) have all been used to estimate micro-level FGT poverty indicators (Foster, Greer, and Thorbecke, 1984). These methods vary in terms of their underlying model assumptions particularly differences in consideration of random effects. This thesis provides results from a numerical comparison of the statistical performance of these three methodologies in the context of a realistic simulation scenario based on a recent Bangladesh poverty study. This comparison study shows that the ELL method is the better performer in terms of relative bias but also significantly underestimates the MSEs of its small area poverty estimates when its underlying area homogeneity assumption is violated. A modified MSE estimation method for ELL-type poverty estimates is therefore developed in this thesis. This method is robust to the presence of significant unexplained between-area variability in the income distribution. This ELL-based MSE estimation methodology is based on a separate bootstrap procedure for MSE estimation, where a correction factor is v used to generate cluster-specific random errors that capture the potential between-area variability unaccounted for by the explanatory variables in the ELL regression model

Recent Developments in the Econometrics of Program Evaluation

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades much research has been done on the econometric and statistical analysis of the effects of such programs or treatments. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization and other areas of empirical micro-economics. In this review we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.program evaluation, causality, unconfoundedness, Rubin Causal Model, potential outcomes, instrumental variables

Exploiting regional treatment intensity for the evaluation of labour market policies

We estimate the effects of active labour market policies (ALMP) on subsequent employment by nonparametric instrumental variables and matching estimators. Very informative administrative Swiss data with detailed regional information are combined with exogenous regional variation in programme participation probabilities, which generate an instrument within well-defined local labour markets. This allows pursuing instrumental variable as well as matching estimation strategies. A specific combination of those methods identifies a new type of effect heterogeneity. We find that ALMP increases individual employment probabilities by about 15% in the short term for unemployed that may be called 'marginal' participants. The effects seem to be considerably smaller for those unemployed not marginal to the participation decision.Local average treatment effect, conditional local IV, active labour market policy, state borders, geographic variation, Switzerland, Fuller estimator

Recent developments in the econometrics of program evaluation

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades much research has been done on the econometric and statistical analysis of the effects of such programs or treatments. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization and other areas of empirical micro-economics. In this review we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.

Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys

In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio‐temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio‐temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014