4,749 research outputs found
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
We often seek to estimate the impact of an exposure naturally occurring or
randomly assigned at the cluster-level. For example, the literature on
neighborhood determinants of health continues to grow. Likewise, community
randomized trials are applied to learn about real-world implementation,
sustainability, and population effects of interventions with proven
individual-level efficacy. In these settings, individual-level outcomes are
correlated due to shared cluster-level factors, including the exposure, as well
as social or biological interactions between individuals. To flexibly and
efficiently estimate the effect of a cluster-level exposure, we present two
targeted maximum likelihood estimators (TMLEs). The first TMLE is developed
under a non-parametric causal model, which allows for arbitrary interactions
between individuals within a cluster. These interactions include direct
transmission of the outcome (i.e. contagion) and influence of one individual's
covariates on another's outcome (i.e. covariate interference). The second TMLE
is developed under a causal sub-model assuming the cluster-level and
individual-specific covariates are sufficient to control for confounding.
Simulations compare the alternative estimators and illustrate the potential
gains from pairing individual-level risk factors and outcomes during
estimation, while avoiding unwarranted assumptions. Our results suggest that
estimation under the sub-model can result in bias and misleading inference in
an observational setting. Incorporating working assumptions during estimation
is more robust than assuming they hold in the underlying causal model. We
illustrate our approach with an application to HIV prevention and treatment
Nonparametric Bounds and Sensitivity Analysis of Treatment Effects
This paper considers conducting inference about the effect of a treatment (or
exposure) on an outcome of interest. In the ideal setting where treatment is
assigned randomly, under certain assumptions the treatment effect is
identifiable from the observable data and inference is straightforward.
However, in other settings such as observational studies or randomized trials
with noncompliance, the treatment effect is no longer identifiable without
relying on untestable assumptions. Nonetheless, the observable data often do
provide some information about the effect of treatment, that is, the parameter
of interest is partially identifiable. Two approaches are often employed in
this setting: (i) bounds are derived for the treatment effect under minimal
assumptions, or (ii) additional untestable assumptions are invoked that render
the treatment effect identifiable and then sensitivity analysis is conducted to
assess how inference about the treatment effect changes as the untestable
assumptions are varied. Approaches (i) and (ii) are considered in various
settings, including assessing principal strata effects, direct and indirect
effects and effects of time-varying exposures. Methods for drawing formal
inference about partially identified parameters are also discussed.Comment: Published in at http://dx.doi.org/10.1214/14-STS499 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Deep Causal Learning for Robotic Intelligence
This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence
The economic impact of international remittances on poverty and household consumption and investment in Indonesia
This paper analyzes the impact of international remittances on poverty and household consumption and investment using panel data (2000 and 2007) from the Indonesian Family Life Survey. Three key findings emerge. First, using an instrumental variables approach to control for selection and endogeneity, it finds that international remittances have a large statistical effect on reducing poverty in Indonesia. Second, households receiving remittances in 2007 spent more at the margin on one key consumption good -- food -- compared with what they would have spent on this good without the receipt of remittances. Third, households receiving remittances in 2007 spent less at the margin on one important investment good -- housing -- compared with what they would have spent on this good without the receipt of remittances. Households receiving international remittances in Indonesia are poorer than other types of households, and thus they tend to spend their remittances at the margin on consumption rather than investment goods.Population Policies,Debt Markets,Remittances,Small Area Estimation Poverty Mapping,Rural Poverty Reduction
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