scholarly journals Probabilistic Reasoning Across the Causal Hierarchy

2020 ◽  
Vol 34 (06) ◽  
pp. 10170-10177 ◽  
Author(s):  
Duligur Ibeling ◽  
Thomas Icard
Keyword(s):  
Bayesian Inference ◽  
Conditional Independence ◽  
Probabilistic Reasoning ◽  
Causal Models ◽  
Causal Effects ◽  
Polynomial Space ◽  
The Third ◽  
Probabilistic Programs

We propose a formalization of the three-tier causal hierarchy of association, intervention, and counterfactuals as a series of probabilistic logical languages. Our languages are of strictly increasing expressivity, the first capable of expressing quantitative probabilistic reasoning—including conditional independence and Bayesian inference—the second encoding do-calculus reasoning for causal effects, and the third capturing a fully expressive do-calculus for arbitrary counterfactual queries. We give a corresponding series of finitary axiomatizations complete over both structural causal models and probabilistic programs, and show that satisfiability and validity for each language are decidable in polynomial space.

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 The Third Wave and the Third World

Pneuma ◽  
2015 ◽  
Vol 37 (2) ◽  
pp. 177-200 ◽  
Author(s):  
Jon Bialecki
Keyword(s):  
United States ◽  
Third World ◽  
The United States ◽  
Causal Effects ◽  
Third Wave ◽  
Science Literature ◽  
The Third ◽  
Social Science Literature ◽  
The Third World ◽  
Charismatic Christianity

While a great deal of social science literature has examined the explosion of pentecostal and charismatic Christianity in the Global South as well as conservative and anti-modern forms of resurgent Christianity in the United States, little work has been done to investigate the causal effects of the former on the latter. Drawing from existing literature, interviews, and archives, this article contributes to filling that gap by arguing that in the mid-twentieth century, evangelical missionary concerns about competition from global Pentecostalism led to an intellectual crisis at the Fuller School of World Missions; this crisis in turn influenced important Third Wave figures such as John Wimber and C. Peter Wagner and is linked to key moments and developments in their thought and pedagogy.

scholarly journals Formal verification of higher-order probabilistic programs: reasoning about approximation, convergence, Bayesian inference, and optimization

2019 ◽  
Vol 3 (POPL) ◽  
pp. 1-30 ◽  
Author(s):  
Tetsuya Sato ◽  
Alejandro Aguirre ◽  
Gilles Barthe ◽  
Marco Gaboardi ◽  
Deepak Garg ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Formal Verification ◽  
Higher Order ◽  
Probabilistic Programs

scholarly journals Estimation of causal effects using linear non-Gaussian causal models with hidden variables

2008 ◽  
Vol 49 (2) ◽  
pp. 362-378 ◽  
Author(s):  
Patrik O. Hoyer ◽  
Shohei Shimizu ◽  
Antti J. Kerminen ◽  
Markus Palviainen
Keyword(s):  
Hidden Variables ◽  
Causal Models ◽  
Causal Effects ◽  
Non Gaussian

scholarly journals How many could have been saved? Effects of social distancing on COVID-19

2021 ◽  
Vol 55 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Matheus Cunha ◽  
Amanda Domingos ◽  
Virginia Rocha ◽  
Marcus Torres
Keyword(s):  
Causal Effect ◽  
Cumulative Effects ◽  
Causal Effects ◽  
Main Argument ◽  
Short Term ◽  
Cross Sectional ◽  
Social Distancing ◽  
The Third ◽  
The World ◽  
Available Information

Abstract What is the effect of social distancing policies on the spread of the new coronavirus? Social distancing policies rose to prominence as most capable of containing contagion and saving lives. Our purpose in this paper is to identify the causal effect of social distancing policies on the number of confirmed cases of COVID-19 and on contagion velocity. We align our main argument with the existing scientific consensus: social distancing policies negatively affect the number of cases. To test this hypothesis, we construct a dataset with daily information on 78 affected countries in the world. We compute several relevant measures from publicly available information on the number of cases and deaths to estimate causal effects for short-term and cumulative effects of social distancing policies. We use a time-series cross-sectional matching approach to match countries’ observable histories. Causal effects (ATTs and ATEs) can be extracted via a dif-in-dif estimator. Results show that social distancing policies reduce the aggregated number of cases by 4,832 on average (or 17.5/100 thousand), but only when strict measures are adopted. This effect seems to manifest from the third week onwards.

scholarly journals Linear Markovian models for lag exposure assessment

2018 ◽  
Vol 55 (2) ◽  
pp. 179-195 ◽  
Author(s):  
Alessandro Magrini
Keyword(s):  
Linear Regression ◽  
Exposure Assessment ◽  
Causal Models ◽  
Causal Effects ◽  
Regression Coefficients ◽  
Standard Approach ◽  
General Context ◽  
Time Lags ◽  
Markovian Models ◽  
Distributed Lag

SummaryLinear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem.

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.

Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose (Preprint)

2021 ◽  
Author(s):  
Mohammad Arif Ul Alam
Keyword(s):  
Drug Overdose ◽  
Causal Effect ◽  
Association Studies ◽  
Causal Models ◽  
Causal Effects ◽  
Opioid Overdose ◽  
Critical Question ◽  
Electronic Health Record Data ◽  
Drug Usage ◽  
Health Crisis

BACKGROUND Drug overdose has become a public health crisis in United States with devastating consequences. However, most of the drug overdose incidences are the consequence of recitative polysubstance usage over a defined period of time which can be happened by either the intentional usage of required drug with other drugs or by accident. Thus, predicting the effects of polysubstance usage is extremely important for clinicians to decide which combination of drugs should be prescribed. Although, machine learning community has made great progress toward using such rich models for supervised prediction, precision medicine problem such as polysubstance usage effects on drug overdose requires heterogeneous causal models, for which there is significantly less theoretical and practical guidance available. Recent advancement of structural causal models can provide ample insights of causal effects from observational data via identifiable causal directed graphs. OBJECTIVE Develop a system to identify heterogeneous causal effect of polysubstance usage from large electronic health record data METHODS We propose a system to estimate heterogeneous concurrent drug usage effects on overdose estimation, that consists of efficient co-variate selection, subgroup selection, generation of and heterogeneous causal effect estimation. Although, there has been several association studies have been proposed in the state-of-art methods, heterogeneous causal effects have never been studied in concurrent drug usage and drug overdose problem. We apply our framework to answer a critical question, ”can concurrent usage of benzodiazepines and opioids has heterogeneous causal effects on opioid overdose epidemic?” RESULTS Using Truven MarketScan claim data collected from 2001 to 2013 have shown significant promise of our proposed framework’s efficacy. Our efficient causal inference model estimated that the causal effect is higher (19%) than the regression studies (15%) to estimate the risks associated with the concurrent usage of opioid and benzodiazepines on opioid overdose. CONCLUSIONS Our generic framework can be a foundation of investigating concurrent events’ causal effects on any outcome that involves heterogeneity

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