scholarly journals Optimizing Consistency and Coverage in Configurational Causal Modeling

2021 ◽  
pp. 004912412199555
Author(s):  
Michael Baumgartner ◽  
Mathias Ambühl
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Comparative Methods ◽  
Causal Models ◽  
Causal Modeling ◽  
Model Fit

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data [Formula: see text], so far, is a matter of repeatedly applying CCMs to [Formula: see text] while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from [Formula: see text]. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

scholarly journals Robustness and Model Selection in Configurational Causal Modeling

2021 ◽  
pp. 004912412098620
Author(s):  
Veli-Pekka Parkkinen ◽  
Michael Baumgartner
Keyword(s):  
Model Selection ◽  
Causal Model ◽  
Real Life ◽  
Comparative Methods ◽  
Causal Modeling ◽  
Life Data ◽  
Real Life Data ◽  
Causal Structures ◽  
Precise Assessment

In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal structures, rendering it impossible to determine exactly how they influence the correctness of selected models. This article develops a computable criterion of fit-robustness, which quantifies the degree to which a CCM model agrees with other models inferred from the same data under systematically varied threshold settings of fit parameters. Based on two extended series of inverse search trials on data simulated from known causal structures, the article moreover provides a precise assessment of the degree to which fit-robustness scoring is conducive to finding a correct causal model and how it compares to other approaches of model selection.

Case Selection and Hypothesis Testing

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Qualitative Research ◽  
Causal Inference ◽  
Hypothesis Testing ◽  
Quantitative Research ◽  
Case Study Research ◽  
Causal Models ◽  
Study Research ◽  
Case Selection ◽  
Study Selection

This chapter discusses quantitative and qualitative practices of case-study selection when the goal of the analysis is to evaluate causal hypotheses. More specifically, it considers how the different causal models used in the qualitative and quantitative research cultures shape the kind of cases that provide the most leverage for hypothesis testing. The chapter examines whether one should select cases based on their value on the dependent variable. It also evaluates the kinds of cases that provide the most leverage for causal inference when conducting case-study research. It shows that differences in research goals between quantitative and qualitative scholars yield distinct ideas about best strategies of case selection. Qualitative research places emphasis on explaining particular cases; quantitative research does not.

scholarly journals Causal inference under over-simplified longitudinal causal models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lola Étiévant ◽  
Vivian Viallon
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Repeated Measurements ◽  
Causal Models ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Time Varying ◽  
Weighted Averages

Abstract Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether – and how – causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.

scholarly journals Relative model fit does not predict topological accuracy in single-gene protein phylogenetics

2019 ◽  
Author(s):  
Stephanie J. Spielman
Keyword(s):  
Model Selection ◽  
Best Practice ◽  
Goodness Of Fit ◽  
Single Gene ◽  
Phylogenetic Reconstruction ◽  
Evolutionary Model ◽  
Specific Model ◽  
Model Fit ◽  
Real Sequence ◽  
Inference Models

AbstractIt is regarded as best practice in phylogenetic reconstruction to perform relative model selection to determine an appropriate evolutionary model for the data. This procedure ranks a set of candidate models according to their goodness-of-fit to the data, commonly using an information theoretic criterion. Users then specify the best-ranking model for inference. While it is often assumed that better-fitting models translate to increase accuracy, recent studies have shown that the specific model employed may not substantially affect inferences. We examine whether there is a systematic relationship between relative model fit and topological inference accuracy in protein phylogenetics, using simulations and real sequences. Simulations employed site-heterogeneous mechanistic codon models that are distinct from protein-level phylogenetic inference models. This strategy allows us to investigate how protein models performs when they are mis-specified to the data, as will be the case for any real sequence analysis. We broadly find that phylogenies inferred across models with vastly different fits to the data produce highly consistent topologies. We additionally find that all models infer similar proportions of false positive splits, raising the possibility that all available models of protein evolution are similarly misspecified. Moreover, we find that the parameter-rich GTR model, whose amino-acid exchangeabilities are free parameters, performs similarly to models with fixed exchangeabilities, although the inference precision associated with GTR models was not examined. We conclude that, while relative model selection may not hinder phylogenetic analysis on protein data, it may not offer specific predictable improvements and is not a reliable proxy for accuracy.

scholarly journals The Bayesian Causal Effect Estimation Algorithm

2015 ◽  
Vol 3 (2) ◽  
pp. 207-236 ◽  
Author(s):  
Denis Talbot ◽  
Geneviève Lefebvre ◽  
Juli Atherton
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Bayesian Model Averaging ◽  
Causal Effect ◽  
Model Averaging ◽  
Estimation Algorithm ◽  
Continuous Exposure ◽  
Statistical Model Selection ◽  
Effect Estimation ◽  
Adjusted Model

AbstractEstimating causal exposure effects in observational studies ideally requires the analyst to have a vast knowledge of the domain of application. Investigators often bypass difficulties related to the identification and selection of confounders through the use of fully adjusted outcome regression models. However, since such models likely contain more covariates than required, the variance of the regression coefficient for exposure may be unnecessarily large. Instead of using a fully adjusted model, model selection can be attempted. Most classical statistical model selection approaches, such as Bayesian model averaging, do not readily address causal effect estimation. We present a new model averaged approach to causal inference, Bayesian causal effect estimation (BCEE), which is motivated by the graphical framework for causal inference. BCEE aims to unbiasedly estimate the causal effect of a continuous exposure on a continuous outcome while being more efficient than a fully adjusted approach.

The Survivor Problem Continued: Introduction to Bayesian Model Selection

2019 ◽  
pp. 308-324
Author(s):  
Therese M. Donovan ◽  
Ruth M. Mickey
Keyword(s):  
Parameter Estimation ◽  
Model Selection ◽  
Bayesian Model ◽  
Formal Education ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Bayesian Model Selection ◽  
Assessment Model ◽  
Model Fit ◽  
Model Assessment

This chapter provides a very brief introduction to Bayesian model selection. The “Survivor Problem” is expanded in this chapter, where the focus is now on comparing two models that predict how long a contestant will last in a game of Survivor: one model uses years of formal education as a predictor, and a second model uses grit as a predictor. Gibbs sampling is used for parameter estimation. Deviance Information Criterion (commonly abbreviated as DIC) is used as a guide for model selection. Details of how this measure is computed are described. The chapter also discusses model assessment (model fit) and Occam’s razor.

scholarly journals Data‐adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss‐based estimation

Biometrics ◽  
2019 ◽  
Vol 76 (1) ◽  
pp. 145-157
Author(s):  
Mireille E. Schnitzer ◽  
Joel Sango ◽  
Steve Ferreira Guerra ◽  
Mark J. Laan
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Minimum Loss ◽  
Longitudinal Model ◽  
Data Adaptive

Causal Inference and Comparative Methods

1986 ◽  
Vol 19 (3) ◽  
pp. 415-437 ◽  
Author(s):  
E. GENE DeFELICE
Keyword(s):  
Qualitative Methods ◽  
Causal Inference ◽  
Survey Research ◽  
Comparative Politics ◽  
Comparative Method ◽  
Comparative Methods ◽  
Alternative Method

Various ways are considered to infer causality from a relatively small number of cases that can be selected but not manipulated. The development of the “comparable-cases strategy” is examined first, together with the claim that it constitutes the comparative method. Mill's “method of agreement” is then presented as an alternative method of comparison, a method that not only can and has been used quite effectively with survey research in comparative politics, but one that is completely free from the methodological short-comings attributed to it. Cases, in short, may be selected for their similarity or their contrast. But because both of these qualitative methods of comparison (even when used jointly) are considerably less powerful than the “method of concomitant variation,” a third strategy is proffered to comparativists. It is a strategy employed extensively by Durkheim, but apparently lost sight of in attempts to reduce it—as well as the method of agreement—to a single comparative method based upon comparable cases.

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