scholarly journals A Selection Bias Approach to Sensitivity Analysis for Causal Effects

2014 ◽  
Vol 22 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Matthew Blackwell
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Selection Bias ◽  
Large Volume ◽  
Quantitative Approach ◽  
Causal Effects ◽  
Omitted Variables ◽  
Fundamental Fact ◽  
Unmeasured Confounders

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data

2013 ◽  
Vol 21 (2) ◽  
pp. 233-251 ◽  
Author(s):  
Walter R. Mebane ◽  
Paul Poast
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Treatment Effect ◽  
Bayesian Method ◽  
Treatment Effects ◽  
Applied Research ◽  
Average Treatment Effect ◽  
Potential Importance ◽  
Treatment Data

How a treatment causes a particular outcome is a focus of inquiry in political science. When treatment data are either nonrandomly assigned or missing, the analyst will often invoke ignorability assumptions: that is, both the treatment and missingness are assumed to be as if randomly assigned, perhaps conditional on a set of observed covariates. But what if these assumptions are wrong? What if the analyst does not know why—or even if—a particular subject received a treatment? Building on Manski, Molinari offers an approach for calculating nonparametric identification bounds for the average treatment effect of a binary treatment under general missingness or nonrandom assignment. To make these bounds substantively more informative, Molinari's technique permits adding monotonicity assumptions (e.g., assuming that treatment effects are weakly positive). Given the potential importance of these assumptions, we develop a new Bayesian method for performing sensitivity analysis regarding them. This sensitivity analysis allows analysts to interpret the assumptions' consequences quantitatively and visually. We apply this method to two problems in political science, highlighting the method's utility for applied research.

Causal inference via string diagram surgery

2021 ◽  
pp. 1-22
Author(s):  
Bart Jacobs ◽  
Aleks Kissinger ◽  
Fabio Zanasi
Keyword(s):  
Causal Inference ◽  
Probabilistic Reasoning ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Causal Effects ◽  
Stochastic Matrices ◽  
Counterfactual Reasoning ◽  
Special Cases ◽  
String Diagram ◽  
Set Up

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

scholarly journals Jakościowa analiza porównawcza (QCA) jako nowa metoda badawcza w nauce o polityce: zarys zagadnienia

2021 ◽  
pp. 99-124
Author(s):  
Przemysław Potocki ◽  
Izabela Lassota
Keyword(s):  
Comparative Analysis ◽  
Political Science ◽  
Research Method ◽  
Qualitative Comparative Analysis ◽  
Quantitative Approach ◽  
Theoretical Assumptions

The article presents main theoretical assumptions and empirical implementations of Qualitative Comparative Analysis (QCA). The main phases of this research method, as the alternative to the quantitative approach which is applied in political science are described. Strengths and weaknesses of this method are described from the perspective of epistemological value obtained by the user of QCA method. Some Polish and foreign examples of QCA implementation are also indicated.

scholarly journals Heterogeneous Causal Effects and Sample Selection Bias

2015 ◽  
Vol 2 ◽  
pp. 351-369 ◽  
Author(s):  
Richard Breen ◽  
Seungsoo Choi ◽  
Anders Holm
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Causal Effects ◽  
Sample Selection Bias

Complex systems models for causal inference in social epidemiology

2020 ◽  
Vol 75 (7) ◽  
pp. 702-708
Author(s):  
Hiba N Kouser ◽  
Ruby Barnard-Mayers ◽  
Eleanor Murray
Keyword(s):  
Complex Systems ◽  
Causal Inference ◽  
Social Inequalities ◽  
Social Epidemiology ◽  
Causal Effects ◽  
Natural Choice ◽  
Systems Models ◽  
Multi Level

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

scholarly journals Commentary: Causal Inference for Social Exposures

2019 ◽  
Vol 40 (1) ◽  
pp. 7-21 ◽  
Author(s):  
Jay S. Kaufman
Keyword(s):  
Observational Study ◽  
Causal Inference ◽  
Human Health ◽  
Social Epidemiology ◽  
Social Institutions ◽  
Causal Effects ◽  
Advantages And Disadvantages ◽  
The Cost

Social epidemiology seeks to describe and quantify the causal effects of social institutions, interactions, and structures on human health. To accomplish this task, we define exposures as treatments and posit populations exposed or unexposed to these well-defined regimens. This inferential structure allows us to unambiguously estimate and interpret quantitative causal parameters and to investigate how these may be affected by biases such as confounding. This paradigm has been challenged recently by some critics who favor broadening the exposures that may be studied beyond treatments to also consider states. Defining the exposure protocol of an observational study is a continuum of specificity, and one may choose to loosen this definition, incurring the cost of causal parameters that become commensurately more vague. The advantages and disadvantages of broader versus narrower definitions of exposure are matters of continuing debate in social epidemiology as in other branches of epidemiology.

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