Causal Inference with Networked Treatment Diffusion

2018 ◽  
Vol 48 (1) ◽  
pp. 152-181 ◽  
Author(s):  
Weihua An
Keyword(s):  
Causal Inference ◽  
Treatment Effect ◽  
Simple Structure ◽  
Smoking Prevention ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Statistical Inferences ◽  
Social Settings ◽  
Biased Estimates

Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends). In this paper, I highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I show that with accurate measures of treatment interference, we can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, treatment interference effect, and treatment effect on interference. I illustrate the methods through a case study of a social network–based smoking prevention intervention.

How Much Is Minnesota Like Wisconsin? Assumptions and Counterfactuals in Causal Inference with Observational Data

2013 ◽  
Vol 21 (2) ◽  
pp. 193-216 ◽  
Author(s):  
Luke Keele ◽  
William Minozzi
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effects ◽  
Empirical Literature ◽  
Careful Evaluation ◽  
Research Designs

Political scientists are often interested in estimating causal effects. Identification of causal estimates with observational data invariably requires strong untestable assumptions. Here, we outline a number of the assumptions used in the extant empirical literature. We argue that these assumptions require careful evaluation within the context of specific applications. To that end, we present an empirical case study on the effect of Election Day Registration (EDR) on turnout. We show how different identification assumptions lead to different answers, and that many of the standard assumptions used are implausible. Specifically, we show that EDR likely had negligible effects in the states of Minnesota and Wisconsin. We conclude with an argument for stronger research designs.

scholarly journals Causal inference under multiple versions of treatment

2013 ◽  
Vol 1 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Tyler J. VanderWeele ◽  
Miguel A. Hernan
Keyword(s):  
Causal Inference ◽  
Treatment Effect ◽  
Indirect Effects ◽  
Causal Effect ◽  
Potential Outcomes ◽  
Direct And Indirect Effects ◽  
Treatment Variable ◽  
Effect Of Treatment ◽  
New Treatment ◽  
Potential Outcomes Framework

Abstract: In this article, we discuss causal inference when there are multiple versions of treatment. The potential outcomes framework, as articulated by Rubin, makes an assumption of no multiple versions of treatment, and here we discuss an extension of this potential outcomes framework to accommodate causal inference under violations of this assumption. A variety of examples are discussed in which the assumption may be violated. Identification results are provided for the overall treatment effect and the effect of treatment on the treated when multiple versions of treatment are present and also for the causal effect comparing a version of one treatment to some other version of the same or a different treatment. Further identification and interpretative results are given for cases in which the version precedes the treatment as when an underlying treatment variable is coarsened or dichotomized to create a new treatment variable for which there are effectively “multiple versions”. Results are also given for effects defined by setting the version of treatment to a prespecified distribution. Some of the identification results bear resemblance to identification results in the literature on direct and indirect effects. We describe some settings in which ignoring multiple versions of treatment, even when present, will not lead to incorrect inferences.

Opening the Blackbox of Treatment Interference: Tracing Treatment Diffusion through Network Analysis

2019 ◽  
pp. 004912411985238 ◽  
Author(s):  
Weihua An ◽  
Tyler J. VanderWeele
Keyword(s):  
Causal Inference ◽  
Smoking Prevention ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Exponential Random Graph ◽  
Social Settings ◽  
Concrete Form ◽  
Central Interest ◽  
Causal Treatment ◽  
Diffusion Networks

Causal inference under treatment interference is a challenging but important problem. Past studies usually make strong assumptions on the structure of treatment interference in order to estimate causal treatment effects while accounting for the effect of treatment interference. In this article, we view treatment diffusion as a concrete form of treatment interference that is prevalent in social settings and also as an outcome of central interest. Specifically, we analyze data from a smoking prevention intervention conducted with 4,094 students in six middle schools in China. We measure treatment interference by tracing how the distributed intervention brochures are shared by students, which provides information to construct the so-called treatment diffusion networks. Besides providing descriptive analyses, we use exponential random graph models to model the treatment diffusion networks in order to reveal covariates and network processes that significantly correlate with treatment diffusion. We show that the findings provide an empirical basis to evaluate previous assumptions on the structure of treatment interference, are informative for imputing treatment diffusion data that is crucial for making causal inference under treatment interference, and shed light on how to improve designs of future interventions that aim to optimize treatment diffusion.

Business Incubation in Uganda, Services, Processes and Incubatee Perceptions- A case study

10.33117/514 ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 94-108
Keyword(s):  
Social Enterprise ◽  
Single Case ◽  
Business Incubation ◽  
Entrepreneurial Networks ◽  
Private Sector Development ◽  
Statistical Inferences ◽  
Office Space ◽  
Interview Methods ◽  
Entrepreneurial Training

Purpose-This paper examines the nature of services and processes of business incubation. Its specific objectives are to establish the nature of services offered by business incubation centers in Uganda, examine the incubation process and to establish the perception of business incu- batees about business incubation services using a case of FinAfrica a private social enterprise. Methodology-This paper presents findings from one incubation center FinAfrica as a case study. Ethnographic design is adopted while observation and interview methods are used to collect data. Results-Key services offered by FinAfrica incubation center include entrepreneurial training, provision of office space, legal and accounting services, mentoring, coaching, entrepreneurial networks and general office administration. The centre has a unique business incubation model which starts with motivating people to start businesses, capacity building, business registration, and ends with graduation after attaining capability for self-sustainability. Incubatees perceive the services offered by the incubation centre as helpful through training, affordable office space, entrepreneurial ecosystem and opportunities for a lean startup. Implications- While this study does not offer statistical inferences for generalisation because of the qualitative design and single case, the exploration of FinAfrica provides insights about how Incubation centers need to plan for positive and sustainable entrepreneurial impact for startups. There is need for more Government and other development partners’ involvement in business incubation and post incubation support for competiveness and growth. Originality/value- This study provides insights about the key services offered in the incubation process and provides insights into the perceived benefits of business incubation. It also contributes to literature about business incubation with practical evidence from an emerging economy whose focus is on private sector development and innovation promotion.

scholarly journals When Housing and Communities Were Delivered: A Case Study of Post-Wenchuan Earthquake Rural Reconstruction and Recovery

2021 ◽  
Vol 13 (14) ◽  
pp. 7629
Author(s):  
Haorui Wu
Keyword(s):  
Built Environment ◽  
Wenchuan Earthquake ◽  
Place Making ◽  
Sustainable Rural Development ◽  
Residential Communities ◽  
Social Settings ◽  
Disaster Survivors ◽  
Two Phases

This study contributes to an in-depth examination of how Wenchuan earthquake disaster survivors utilize intensive built environment reconstruction outcomes (housing and infrastructural systems) to facilitate their long-term social and economic recovery and sustainable rural development. Post-disaster recovery administered via top-down disaster management systems usually consists of two phases: a short-term, government-led reconstruction (STGLR) of the built environment and a long-term, survivor-led recovery (LTSLR) of human and social settings. However, current studies have been inadequate in examining how rural disaster survivors have adapted to their new government-provided housing or how communities conducted their long-term recovery efforts. This qualitative case study invited sixty rural disaster survivors to examine their place-making activities utilizing government-delivered, urban-style residential communities to support their long-term recovery. This study discovered that rural residents’ recovery activities successfully perpetuated their original rural lives and rebuilt social connections and networks both individually and collectively. However, they were only able to manage their agriculture-based livelihood recovery temporarily. This research suggests that engaging rural inhabitants’ place-making expertise and providing opportunities to improve their housing and communities would advance the long-term grassroots recovery of lives and livelihoods, achieving sustainable development.

Innovative promotional strategies and diversification of flavoured mass merchandise cigar products: a case study of Swedish match

2021 ◽  
pp. tobaccocontrol-2020-056145 ◽  
Author(s):  
Ollie Ganz ◽  
Mary Hrywna ◽  
Kevin R J Schroth ◽  
Cristine D Delnevo
Keyword(s):  
Regulatory Authority ◽  
Business Practice ◽  
Smoking Prevention ◽  
Tobacco Products ◽  
The Family ◽  
Promotional Efforts ◽  
Promotional Strategies ◽  
Definition Of ◽  
The U.S

In 2009, the Family Smoking Prevention and Tobacco Control Act (TCA) granted the U.S. Food and Drug Administration (FDA) regulatory authority over tobacco products, although initially this only included cigarettes, smokeless tobacco and roll-your-own tobacco. In 2016, the deeming rule extended regulatory authority to include all tobacco products, including cigars. The deeming rule prohibited the introduction of new tobacco products into the marketplace without proper marketing authorisation and laid out pathways for tobacco companies to follow. The deeming rule should have frozen the cigar marketplace in 2016. In this paper, we describe how the cigarillo marketplace, nevertheless, continues to diversify with new brands, flavors, styles and packaging sizes entering the market regularly. As an example, we highlight recent promotional efforts by Swedish Match North America (Swedish Match) for their popular cigarillo brands, including White Owl, Night Owl and Garcia y Vega’s Game brand. We argue that ambiguities in the TCA make it unclear whether Swedish Match’s seemingly new cigarillos fit the definition of new tobacco products and, if so, whether they are on the market legally. Swedish Match and other cigarillo companies may be taking advantage of these ambiguities to promote a variety of cigarillo flavors and styles in innovative ways. Given that cigars are combustible tobacco products that pose many of the same risks as cigarettes, this business practice raises significant concerns regarding the protection of public health, particularly among young people.

scholarly journals Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data

2016 ◽  
Vol 41 (4) ◽  
pp. 357-388 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Anna Rhodes
Keyword(s):  
Head Start ◽  
Randomized Trial ◽  
Treatment Effect ◽  
External Validity ◽  
Randomized Trials ◽  
Target Population ◽  
Data Sets ◽  
Common Data Elements ◽  
Ex Post

Background: Given increasing concerns about the relevance of research to policy and practice, there is growing interest in assessing and enhancing the external validity of randomized trials: determining how useful a given randomized trial is for informing a policy question for a specific target population. Objectives: This article highlights recent advances in assessing and enhancing external validity, with a focus on the data needed to make ex post statistical adjustments to enhance the applicability of experimental findings to populations potentially different from their study sample. Research design: We use a case study to illustrate how to generalize treatment effect estimates from a randomized trial sample to a target population, in particular comparing the sample of children in a randomized trial of a supplemental program for Head Start centers (the Research-Based, Developmentally Informed study) to the national population of children eligible for Head Start, as represented in the Head Start Impact Study. Results: For this case study, common data elements between the trial sample and population were limited, making reliable generalization from the trial sample to the population challenging. Conclusions: To answer important questions about external validity, more publicly available data are needed. In addition, future studies should make an effort to collect measures similar to those in other data sets. Measure comparability between population data sets and randomized trials that use samples of convenience will greatly enhance the range of research and policy relevant questions that can be answered.

Biased estimates of treatment effect in randomized trials

1984 ◽  
Vol 5 (3) ◽  
pp. 303 ◽  
Author(s):  
Mitchell Gail ◽  
Sam Wieand ◽  
Steven Piantadosi
Keyword(s):  
Treatment Effect ◽  
Randomized Trials ◽  
Biased Estimates

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.

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