scholarly journals Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

2014 ◽  
Vol 2 (2) ◽  
pp. 187-199 ◽  
Author(s):  
Xavier de Luna ◽  
Per Johansson
Keyword(s):  
Simulation Study ◽  
Instrumental Variable ◽  
Observational Studies ◽  
Causal Effect ◽  
Causal Effects ◽  
Nonparametric Identification ◽  
Average Causal Effect

AbstractThe identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.

scholarly journals Novel bounds for causal effects based on sensitivity parameters on the risk difference scale

2021 ◽  
Vol 9 (1) ◽  
pp. 190-210
Author(s):  
Arvid Sjölander ◽  
Ola Hössjer
Keyword(s):  
Risk Ratio ◽  
Simulation Study ◽  
Observational Studies ◽  
Causal Effect ◽  
Real Data ◽  
Risk Difference ◽  
Causal Effects ◽  
Ratio Scale ◽  
Unmeasured Confounding ◽  
Range Of Values

Abstract Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

scholarly journals Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation

2020 ◽  
Vol 102 (2) ◽  
pp. 355-367
Author(s):  
Gerard J. van den Berg ◽  
Petyo Bonev ◽  
Enno Mammen
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Asymptotic Properties ◽  
Right Censoring ◽  
Unemployment Duration ◽  
Dynamic Selection ◽  
Average Causal Effect ◽  
Variable Approach ◽  
Dynamic Treatment ◽  
Job Finding

We develop an instrumental variable approach for identification of dynamic treatment effects on survival outcomes in the presence of dynamic selection, noncompliance, and right-censoring. The approach is nonparametric and does not require independence of observed and unobserved characteristics or separability assumptions. We propose estimation procedures and derive asymptotic properties. We apply our approach to evaluate a policy reform in which the pathway of unemployment benefits as a function of the unemployment duration is modified. Those who were unemployed at the reform date could choose between the old and the new regime. We find that the new regime has a positive average causal effect on the job finding rate.

scholarly journals Counterfactual Reasoning in Observational Studies

2019 ◽  
Vol 33 ◽  
pp. 9886-9887
Author(s):  
Negar Hassanpour
Keyword(s):  
Alternative Treatment ◽  
Observational Studies ◽  
Causal Effect ◽  
Intelligent Agent ◽  
Population Based ◽  
Causal Effects ◽  
Medical Procedure ◽  
Counterfactual Reasoning ◽  
Effect Estimation ◽  
The Individual

To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures).

scholarly journals A Proxy Outcome Approach for Causal Effect in Observational Studies: A Simulation Study

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wenbin Liang ◽  
Yuejen Zhao ◽  
Andy H. Lee
Keyword(s):  
Simulation Study ◽  
Observational Studies ◽  
Mental Health Problems ◽  
Causal Effect ◽  
Health Impact ◽  
Specific Knowledge ◽  
System A ◽  
Correct Conclusion ◽  
Alternative Approach ◽  
Estimation System

Background. Known and unknown/unmeasured risk factors are the main sources of confounding effects in observational studies and can lead to false observations of elevated protective or hazardous effects. In this study, we investigate an alternative approach of analysis that is operated on field-specific knowledge rather than pure statistical assumptions.Method. The proposed approach introduces a proxy outcome into the estimation system. A proxy outcome possesses the following characteristics: (i) the exposure of interest is not a cause for the proxy outcome; (ii) causes of the proxy outcome and the study outcome are subsets of a collection of correlated variables. Based on these two conditions, the confounding-effect-driven association between the exposure and proxy outcome can then be measured and used as a proxy estimate for the effects of unknown/unmeasured confounders on the outcome of interest. Performance of this approach is tested by a simulation study, whereby 500 different scenarios are generated, with the causal factors of a proxy outcome and a study outcome being partly overlapped under low-to-moderate correlations.Results. The simulation results demonstrate that the conventional approach only led to a correct conclusion in 21% of the 500 scenarios, as compared to 72.2% for the alternative approach.Conclusion. The proposed method can be applied in observational studies in social science and health research that evaluates the health impact of behaviour and mental health problems.

scholarly journals Data-Adaptive Causal Effects and Superefficiency

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Peter M. Aronow
Keyword(s):  
Causal Inference ◽  
Empirical Distribution ◽  
Causal Effect ◽  
Short Note ◽  
Causal Effects ◽  
Asymptotically Normal ◽  
Average Causal Effect ◽  
Population Average ◽  
Data Adaptive ◽  
Local Average

AbstractRecent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency. These inferential targets are sometimes data-adaptive, in that they are dependent on the empirical distribution of the data. In this short note, we show that if researchers are willing to adapt the inferential target on the basis of efficiency, then extraordinary gains in precision can potentially be obtained. Specifically, when causal effects are heterogeneous, any asymptotically normal and root-$n$ consistent estimator of the population average causal effect is superefficient for a data-adaptive local average causal effect.

scholarly journals Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds

2018 ◽  
Vol 43 (5) ◽  
pp. 540-567 ◽  
Author(s):  
Jiannan Lu ◽  
Peng Ding ◽  
Tirthankar Dasgupta
Keyword(s):  
Causal Effect ◽  
Real Life ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Randomized Experiments ◽  
Sharp Bounds ◽  
Average Causal Effect ◽  
Behavioral Studies ◽  
And Control ◽  
Potential Outcomes Framework

Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the parameter of interest, is difficult to interpret for ordinal outcomes. To address this challenge, we propose to use two causal parameters, which are defined as the probabilities that the treatment is beneficial and strictly beneficial for the experimental units. However, although well-defined for any outcomes and of particular interest for ordinal outcomes, the two aforementioned parameters depend on the association between the potential outcomes and are therefore not identifiable from the observed data without additional assumptions. Echoing recent advances in the econometrics and biostatistics literature, we present the sharp bounds of the aforementioned causal parameters for ordinal outcomes, under fixed marginal distributions of the potential outcomes. Because the causal estimands and their corresponding sharp bounds are based on the potential outcomes themselves, the proposed framework can be flexibly incorporated into any chosen models of the potential outcomes and is directly applicable to randomized experiments, unconfounded observational studies, and randomized experiments with noncompliance. We illustrate our methodology via numerical examples and three real-life applications related to educational and behavioral research.

Estimating Causal Effects From Multilevel Group-Allocation Data

2005 ◽  
Vol 30 (4) ◽  
pp. 397-412 ◽  
Author(s):  
Alix I. Gitelman
Keyword(s):  
Group Membership ◽  
Causal Effect ◽  
Hierarchical Linear Model ◽  
Dynamic Effect ◽  
Educational Interventions ◽  
Causal Effects ◽  
Group Dynamic ◽  
Average Causal Effect ◽  
Effect Of Treatment ◽  
Group Allocation

In group-allocation studies for comparing behavioral, social, or educational interventions, subjects in the same group necessarily receive the same treatment, whereby a group and/or group-dynamic effect can confound the treatment effect. General counterfactual outcomes that depend on group characteristics, group membership, and treatment are developed to provide a structure for specifying causal effects of treatment in the multilevel setting. An average causal effect of treatment cannot be specified, however, without a simplifying assumption of group-membership invariance (i.e., no group-dynamic effect). Under group-membership invariance and ignorability assumptions, the average causal effect is then connected to estimable quantities of the hierarchical linear model (HLM). Furthermore, it is shown that the typical specification of the HLM involves conditional independence assumptions that actually preclude the group-dynamic effect.

scholarly journals Correction to: A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Myra B. McGuinness ◽  
Jessica Kasza ◽  
Amalia Karahalios ◽  
Robyn H. Guymer ◽  
Robert P. Finger ◽  
...  
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Causal Effect ◽  
Comparison Of Methods ◽  
Average Causal Effect

A Framework for Classifying Causal Factors of Tourism Demand Seasonality: An Interseason and Intraseason Approach

2020 ◽  
Vol 44 (5) ◽  
pp. 733-760
Author(s):  
Jorge Ridderstaat ◽  
Robertico Croes
Keyword(s):  
Sustainable Management ◽  
Instrumental Variable ◽  
Causal Effects ◽  
Policy Recommendations ◽  
Tourism Demand ◽  
Panel Unit Root Tests ◽  
Causal Factors ◽  
Instrumental Variable Regression ◽  
Demand Seasonality

This study proposes a framework for classifying the causal effects of tourism demand seasonality, linking between season effects (interseason) with within season effects (intraseason). The literature has typically investigated the causes of tourism demand seasonality as isolated static factors, mostly ignoring the possibility of interseason and intraseason impacts. This study contributes to the literature by providing a system of categorizing the drivers of tourism demand seasonality, by introducing additional causal factors, and by using an alternative seasonal smoothing technique. The methodology includes data decomposition, panel unit root tests, and instrumental variable regression, distinguishing between peak and shoulder seasons. The findings, based on a case study, identify vigorous–irregular and moderate–irregular factors being the most prominent drivers of U.S. tourism demand seasonality, which could assist in specific policy recommendations for more sustainable management of tourism demand seasonality.

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