scholarly journals Identifiability of causal effects with multiple causes and a binary outcome

Biometrika ◽  
2021 ◽  
Author(s):  
Dehan Kong ◽  
Shu Yang ◽  
Linbo Wang
Keyword(s):  
Observational Studies ◽  
Choice Model ◽  
Causal Effect ◽  
Gaussian Model ◽  
Estimation Procedure ◽  
Outcome Variable ◽  
Identification Approach ◽  
Multiple Treatments ◽  
Binary Choice Model ◽  
Probit Link

Abstract Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that the causal effect is indeed identifiable if we assume a general binary choice model for the outcome with a non-probit link. Our identification approach is based on the incongruence between Gaussianity of the treatments and latent confounder, andnon-Gaussianity of a latent outcome variable. We further develop a two-step likelihood-based estimation procedure.

A binary choice model with partial observability for panel data

2020 ◽  
Vol 15 (4) ◽  
pp. 315-322
Author(s):  
Ekaterina Batalova ◽  
Kirill Furmanov ◽  
Ekaterina Shelkova
Keyword(s):  
Job Search ◽  
Latent Class ◽  
Choice Model ◽  
Maximum Likelihood Estimates ◽  
Binary Choice ◽  
Panel Model ◽  
Binary Response Variable ◽  
Monitoring Survey ◽  
Binary Choice Model ◽  
Time Invariant

We consider a panel model with a binary response variable that is a product of two unobservable factors, each determined by a separate binary choice equation. One of these factors is assumed to be time-invariant and may be interpreted as a latent class indicator. A simulation study shows that maximum likelihood estimates from even the shortest panel are much more reliable than those obtained from a cross-section. As an illustrative example, the model is applied to Russian Longitudinal Monitoring Survey data to estimate a proportion of the non-employed population who are participating in job search.

scholarly journals Systematic survey of the causal language use in systematic reviews of observational studies: a study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e038571
Author(s):  
Mi Ah Han ◽  
Gordon Guyatt
Keyword(s):  
Systematic Reviews ◽  
Observational Studies ◽  
Language Use ◽  
Causal Effect ◽  
Data Extraction ◽  
Open Science ◽  
Third Party ◽  
Systematic Survey ◽  
Assessment Development ◽  
Causal Language

IntroductionSometimes, observational studies may provide important evidence that allow inferences of causality between exposure and outcome (although on most occasions only low certainty evidence). Authors, frequently and perhaps usually at the behest of the journals to which they are submitting, avoid using causal language when addressing evidence from observational studies. This is true even when the issue of interest is the causal effect of an intervention or exposure. Clarity of thinking and appropriateness of inferences may be enhanced through the use of language that reflects the issue under consideration. The objectives of this study are to systematically evaluate the extent and nature of causal language use in systematic reviews of observational studies and to relate that to the actual intent of the investigation.Methods and analysisWe will conduct a systematic survey of systematic reviews of observational studies addressing modifiable exposures and their possible impact on patient-important outcomes. We will randomly select 200 reviews published in 2019, stratified in a 1:1 ratio by use and non-use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Teams of two reviewers will independently assess study eligibility and extract data using a standardised data extraction forms, with resolution of disagreement by discussion and, if necessary, by third party adjudication. Through examining the inferences, they make in their papers’ discussion, we will evaluate whether the authors’ intent was to address causation or association. We will summarise the use of causal language in the study title, abstract, study question and results using descriptive statistics. Finally, we will assess whether the language used is consistent with the intention of the authors. We will determine whether results in reviews that did or did not use GRADE differ.Ethics and disseminationEthics approval for this study is not required. We will disseminate the results through publication in a peer-reviewed journals.RegistrationOpen Science Framework (osf.io/vh8yx).

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 Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenglin Duan ◽  
Jingjing Shi ◽  
Guozhen Yuan ◽  
Xintian Shou ◽  
Ting Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity Analysis ◽  
Causal Relationship ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Relationships ◽  
Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

scholarly journals Factors influencing bicycle use: a binary choice model with panel data

2017 ◽  
Vol 27 ◽  
pp. 253-260 ◽  
Author(s):  
Ana Barberan ◽  
João de Abreu e Silva ◽  
Andres Monzon
Keyword(s):  
Panel Data ◽  
Choice Model ◽  
Binary Choice ◽  
Factors Influencing ◽  
Binary Choice Model

scholarly journals The investment project pipeline: cost escalation, lead time, success, failure and speed

2011 ◽  
Vol 36 (3) ◽  
pp. 317-348 ◽  
Author(s):  
Kenneth W Clements ◽  
Jiawei Si
Keyword(s):  
Lead Time ◽  
Choice Model ◽  
Investment Project ◽  
Regulatory Reform ◽  
Lead Times ◽  
Index Number ◽  
Investment Projects ◽  
Binary Choice Model ◽  
Investment Process ◽  
Planning And Construction

As they involve expectations about the future and long lead times for planning and construction, the evolution of investment projects is usually complex and volatile. This paper analyses an important aspect of this volatility by studying the nature of the investment process, from the initial bright idea to the final construction and operational phase of a project. We refer to this process as the ‘project pipeline’. Using a rich source of information on recent Australian resource development projects, an index-number approach is employed to measure the escalation of costs of projects in the pipeline and the time spent there (the lead time). The determinants of the probability of ultimate success of projects is analysed with a binary choice model. Finally, a Markov chain approach is used to model the transitions of projects from one stage in the pipeline to the next, and to examine the implications of regulatory reform that have the effect of speeding up the flow of projects.

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