Search results for "Causal inference"
showing 10 items of 31 documents
Job preservation efforts: when does job insecurity prompt performance?
2020
PurposeWhile job insecurity generally impedes performance, there may be circumstances under which it can prompt performance. The purpose of this paper is to examine a specific situation (reorganization) in which job insecurity may prompt task and contextual performance. The authors propose that performance can represent a job preservation strategy, to which employees may only resort when supervisor-issued ratings of performance are instrumental toward securing one’s job. The authors hypothesize that because of this instrumentality, job insecurity will motivate employees’ performance only when they have low intrinsic motivation, and only when they perceive high distributive justice.Design/me…
Prosociality as a mediator between teacher collaboration and turnover intention
2019
PurposeThe purpose of this paper is to investigate the mediating role of prosociality, which is defined in terms of helping and benefitting others, between teacher collaboration and their turnover intentions. Prosociality was measured as prosocial impact and prosocial motivation.Design/methodology/approachThis study was conducted through a cross-sectional survey of 260 elementary and junior high school teachers in Japan. A structural equational model was employed to examine the mediating roles of prosocial impact and prosocial motivation in the relationships between teacher collaboration and their turnover intention.FindingsThe results, first, supported the hypotheses: the high perception o…
Effects of Grade Retention Policies: A Literature Review of Empirical Studies Applying Causal Inference
2021
The identification of the causal effects of grade retention policies is of enormous relevance for researchers and policymakers alike. Taking advantage of the availability of more detailed longitudinal datasets, researchers have been able to apply different identification strategies that address the classical problems of selection bias and unobserved heterogeneity that have plagued previous studies on the effect of retention. We present a systematic literature review of empirical studies aiming to unveil the causal effects of retention. This study underlines the need to consider and evaluate different kinds of grade retention polices as their effects vary depending on several dimensions (suc…
Application of Inverse-Probability-of-Treatment Weighting to Estimate the Effect of Daytime Sleepiness in Obstructive sleep apnea patients
2022
Continuous positive airway pressure (CPAP), the first line therapy for obstructive sleep apnea (OSA), is considered effective in reducing daytime sleepiness. Its efficacy relies on adequate adherence, often defined as >4 hours per night. However, this binary threshold may limit our understanding of the causal effect of CPAP adherence and daytime sleepiness and multilevel approach for CPAP adherence can be more appropriate.
Causal inference in geosciences with kernel sensitivity maps
2020
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science. In remote sensing and geosciences this is of special relevance to better understand the Earth's system and the complex and elusive interactions between processes. In this paper we explore a framework to derive cause-effect relations from pairs of variables via regression and dependence estimation. We propose to focus on the sensitivity (curvature) of the dependence estimator to account for the asymmetry of the forward and inverse densities of approximation residuals. Results in a large collection of 28 geoscience causal inference problems demonstrate the…
Causal Inference in Geoscience and Remote Sensing From Observational Data
2020
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, …
Pathway analysis of high-throughput biological data within a Bayesian network framework
2011
Abstract Motivation: Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Results: Proposed method takes into account the connectivity and relatedness between nodes of the p…
Methods for evaluating causality in observational studies.
2019
BACKGROUND: In clinical medical research, causality is demonstrated by randomized controlled trials (RCTs). Often, however, an RCT cannot be conducted for ethical reasons, and sometimes for practical reasons as well. In such cases, knowledge can be derived from an observational study instead. In this article, we present two methods that have not been widely used in medical research to date. METHODS: The methods of assessing causal inferences in observational studies are described on the basis of publications retrieved by a selective literature search. RESULTS: Two relatively new approaches—regression-discontinuity methods and interrupted time series—can be used to demonstrate a causal relat…
Bayesian causal mediation analysis through linear mixed-effect models
2022
In mediational settings, the main focus is on the estimation of the indirect effect of an exposure on an outcome through a third variable called mediator. The traditional maximum likelihood estimation method presents several problems in the estimation of the standard error and the confidence interval of the indirect effect. In this paper, we propose a Bayesian approach to obtain the posterior distribution of the indirect effect through MCMC, in the context of mediational mixed models for longitudinal data. A simulation study shows that our method outperforms the traditional maximum likelihood approach in terms of bias and coverage rates.
Enhancing identification of causal effects by pruning
2018
Causal models communicate our assumptions about causes and effects in real-world phe- nomena. Often the interest lies in the identification of the effect of an action which means deriving an expression from the observed probability distribution for the interventional distribution resulting from the action. In many cases an identifiability algorithm may return a complicated expression that contains variables that are in fact unnecessary. In practice this can lead to additional computational burden and increased bias or inefficiency of estimates when dealing with measurement error or missing data. We present graphical criteria to detect variables which are redundant in identifying causal effe…