318 research outputs found
Average treatment effect estimation via random recursive partitioning
A new matching method is proposed for the estimation of the average treatment
effect of social policy interventions (e.g., training programs or health care
measures). Given an outcome variable, a treatment and a set of pre-treatment
covariates, the method is based on the examination of random recursive
partitions of the space of covariates using regression trees. A regression tree
is grown either on the treated or on the untreated individuals {\it only} using
as response variable a random permutation of the indexes 1... ( being the
number of units involved), while the indexes for the other group are predicted
using this tree. The procedure is replicated in order to rule out the effect of
specific permutations. The average treatment effect is estimated in each tree
by matching treated and untreated in the same terminal nodes. The final
estimator of the average treatment effect is obtained by averaging on all the
trees grown. The method does not require any specific model assumption apart
from the tree's complexity, which does not affect the estimator though. We show
that this method is either an instrument to check whether two samples can be
matched (by any method) and, when this is feasible, to obtain reliable
estimates of the average treatment effect. We further propose a graphical tool
to inspect the quality of the match. The method has been applied to the
National Supported Work Demonstration data, previously analyzed by Lalonde
(1986) and others
Invariant and Metric Free Proximities for Data Matching: An R Package
Data matching is a typical statistical problem in non experimental and/or observational studies or, more generally, in cross-sectional studies in which one or more data sets are to be compared. Several methods are available in the literature, most of which based on a particular metric or on statistical models, either parametric or nonparametric. In this paper we present two methods to calculate a proximity which have the property of being invariant under monotonic transformations. These methods require at most the notion of ordering. An open-source software in the form of a R package is also presented.
cem: Software for Coarsened Exact Matching
This program is designed to improve causal inference via a method of matching that is widely applicable in observational data and easy to understand and use (if you understand how to draw a histogram, you will understand this method). The program implements the coarsened exact matching (CEM) algorithm, described below. CEM may be used alone or in combination with any existing matching method. This algorithm, and its statistical properties, are described in Iacus, King, and Porro (2008).
Measuring Social Well Being in The Big Data Era: Asking or Listening?
The literature on well being measurement seems to suggest that "asking" for a
self-evaluation is the only way to estimate a complete and reliable measure of
well being. At the same time "not asking" is the only way to avoid biased
evaluations due to self-reporting. Here we propose a method for estimating the
welfare perception of a community simply "listening" to the conversations on
Social Network Sites. The Social Well Being Index (SWBI) and its components are
proposed through to an innovative technique of supervised sentiment analysis
called iSA which scales to any language and big data. As main methodological
advantages, this approach can estimate several aspects of social well being
directly from self-declared perceptions, instead of approximating it through
objective (but partial) quantitative variables like GDP; moreover
self-perceptions of welfare are spontaneous and not obtained as answers to
explicit questions that are proved to bias the result. As an application we
evaluate the SWBI in Italy through the period 2012-2015 through the analysis of
more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1512.0156
Social networks, happiness and health: from sentiment analysis to a multidimensional indicator of subjective well-being
This paper applies a novel technique of opinion analysis over social media
data with the aim of proposing a new indicator of perceived and subjective
well-being. This new index, namely SWBI, examines several dimension of
individual and social life. The indicator has been compared to some other
existing indexes of well-being and health conditions in Italy: the BES
(Benessere Equo Sostenibile), the incidence rate of influenza and the abundance
of PM10 in urban environments. SWBI is a daily measure available at province
level. BES data, currently available only for 2013 and 2014, are annual and
available at regional level. Flu data are weekly and distributed as regional
data and PM10 are collected daily for different cities. Due to the fact that
the time scale and space granularity of the different indexes varies, we apply
a novel statistical technique to discover nowcasting features and the classical
latent analysis to study the relationships among them. A preliminary analysis
suggest that the environmental and health conditions anticipate several
dimensions of the perception of well-being as measured by SWBI. Moreover, the
set of indicators included in the BES represent a latent dimension of
well-being which shares similarities with the latent dimension represented by
SWBI.Comment: 26 pages, 5 figur
CEM: Coarsened Exact Matching in Stata
We introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In coarsened exact matching, users temporarily coarsen their data, exact match on these coarsened data, and then run their analysis on the uncoarsened, matched data. Coarsened exact matching bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is monotonic imbalance bounding (so that reducing the maximum imbalance on one variable has no effect on others), does not require a separate procedure to restrict data to common support, meets the congruence principle, is approximately invariant to measurement error, balances all nonlinearities and interactions in sample (i.e., not merely in expectation), and works with multiply imputed datasets. Other matching methods inherit many of the coarsened exact matching method’s properties when applied to further match data preprocessed by coarsened exact matching.
Invariant and Metric Free Proximities for Data Matching: An R Package
Data matching is a typical statistical problem in non experimental and/or observational studies or, more generally, in cross-sectional studies in which one or more data sets are to be compared. Several methods are available in the literature, most of which based on a particular metric or on statistical models, either parametric or nonparametric. In this paper we present two methods to calculate a proximity which have the property of being invariant under monotonic transformations. These methods require at most the notion of ordering. An open-source software in the form of a R package is also presented
cem: Coarsened Exact Matching in Stata
This paper introduces a Stata implementation of Coarsened Exact Matching (CEM), a new method for improving the estimation of causal effects by reducing imbalance in co-variates between treated and control groups. CEM is faster, easier to use and understand, requires fewer assumptions, more easily automated, and possesses more attractive statistical properties for many applications than existing matching methods. In CEM, users temporarily coarsen their data, exact match on these coarsened data, then run their analysis on the uncoarsened, matched data. CEM bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is montonic imbalance bounding (so that reducing the maximum imbalance on one variable has no e ect on others), does not require a separate procedure to restrict data to common support, meets the congruence principle, is approximately invariant to measurement error, balances all nonlinearities and interactions in-sample (i.e., not merely in expectation), and works with multiply imputed data sets. Other matching methods inheret [sic] many of CEM's properties when applied to further match data preprocessed by CEM. The library cem implements the CEM algorithm in Stata.Governmen
A proposal to deal with sampling bias in social network big data
[EN] Selection bias is the bias introduced by the non random selection of data, it leads to question whether the sample obtained is representative of the target population. Generally there are different types of selection bias, but when one manages web-surveys or data from social network as Twitter or Facebook, one mostly need to focus with sampling and self-selection bias. In this work we propose to use offcial statistics to anchor and remove the sampling bias and unreliability of the estimations, due to the use of social network big data, following a weighting method combined with a small area estimations (SAE) approach.Iacus, SM.; Porro, G.; Salini, S.; Siletti, E. (2018). A proposal to deal with sampling bias in social network big data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 29-37. https://doi.org/10.4995/CARMA2018.2018.8302OCS293
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