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Author: Claudia Luise Charlotte Noack
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Languages : en
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Author: Claudia Luise Charlotte Noack
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Languages : en
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Author: Michael Pollmann
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Languages : en
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This dissertation explores the estimation of causal effects in settings with non-standard data. In the first chapter, the treatments are not directly assigned to outcome units but instead occur in the same geographic space. In the second chapter, responses to hypothetical questions describing both the treated and control state are used to learn about the effects of a treatment on real behavior (outcomes). In the third chapter, treatments are assigned according to a randomized experiment but outcomes are heavy-tailed, such that semiparametric approaches are useful to improve efficiency and robustness. The first chapter considers settings where the treatments causing the effects of interest are not directly associated with specific units for which we measure outcomes, but rather occur in the same geographic space. Many events and policies (treatments), such as opening of businesses, building of hospitals, and sources of pollution, occur at specific spatial locations, with researchers interested in their effects on nearby individuals or businesses (outcome units). However, the existing treatment effects literature primarily considers treatments that could experimentally be assigned directly at the level of the outcome units, potentially with spillover effects. I approach the spatial treatment setting from a similar experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between individuals near realized treatment locations and individuals near counterfactual (unrealized) candidate locations, which is distinct from current empirical practice. I derive standard errors based on this design-based perspective that are straightforward to compute irrespective of spatial correlations in outcomes. Furthermore, I propose machine learning methods to find counterfactual candidate locations and show how to apply the proposed methods on observational data. I study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 shelter-in-place policies. I find a substantial positive effect at a very short distance. Correctly accounting for possible effect "interference" between grocery stores located close to one another is of first order importance when calculating standard errors in this application. The second chapter is co-authored with B. Douglas Bernheim, Daniel Björkegren, and Jeffrey Naecker. We explore methods for inferring the causal effects of treatments on choices by combining data on real choices with hypothetical evaluations. We propose a class of estimators, identify conditions under which they yield consistent estimates, and derive their asymptotic distributions. The approach is applicable in settings where standard methods cannot be used (e.g., due to the absence of helpful instruments, or because the treatment has not been implemented). It can recover heterogeneous treatment effects more comprehensively, and can improve precision. We provide proof of concept using data generated in a laboratory experiment and through a field application. The final chapter is co-authored with Susan Athey, Peter J. Bickel, Aiyou Chen, and Guido W. Imbens. We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be heavy-tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in recent experimentation in tech companies. We propose using parametric models for the treatment effects, as opposed to parametric models for the full outcome distributions. This leads to semiparametric models for the outcome distributions. We derive the semiparametric efficiency bound for this setting, and propose efficient estimators. In the case with a constant treatment effect one of the proposed estimators has an interesting interpretation as a weighted average of quantile treatment effects, with the weights proportional to (minus) the second derivative of the log of the density of the potential outcomes. Our analysis also results in an extension of Huber's model and trimmed mean to include asymmetry and a simplified condition on linear combinations of order statistics, which may be of independent interest.
Author: Kevin Xinkai Guo
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Languages : en
Pages : 0
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This thesis describes three research projects in causal inference, all related to the problem of contrasting the average counterfactual outcomes on two sides of a binary decision. In the first project, we discuss estimation of the average causal effect in a randomized control trial. Here, we find that statisticians find themselves in a kind of statistical paradise: a simple model-based procedure delivers correct confidence intervals even if the experimental participants are not randomly sampled and mis-specified models are used. In the second project, we consider the problem of testing for a treatment effect using observational data with no hidden confounders. Conceptually, this is no different from a rather complicated RCT, and one might expect that a return to statistical paradise is possible. Unfortunately, this is not the case: we show that even intuitively reasonable uses of correct models may still yield misleading conclusions. The final project looks at observational data with unobserved confounding and gives methods for computing bounds on average causal effects. Here, we discover some never-before-seen robustness properties unique to the partially-identified setting.
Author: Ashutosh Charudatta Bhave
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Category : Causation
Languages : en
Pages : 0
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In my dissertation consisting of three research projects, I focus on solving problems which deal with reliably estimating the impact of a change in policy in quasi-experimental setup. I utilize cutting edge methods in econometrics and machine learning to quantify causal effects of policy changes, understand the mechanism behind the effect and most importantly highlight the implications for the managers and policy makers. My first research paper, “A Study of the Effects of Legalization of Recreational Marijuana on Sales of Cigarettes” attempts to establish a causal link between the legalization of recreational marijuana and the sales of cigarettes in retail stores. Recreational marijuana legalization (RML) has been on the rise in the recent years and many arguments have been put forth to support or counter this move. We explore the possibility of RML impacting cigarette consumption. This is important for understanding the impact on health care expenditures related to smoking, which is about $330 billion in the US. Our results show that in states that have passed RML, there is a 7% increase in cigarette sales. This is an important finding since it reverses a decline in cigarette sales in recent years. Therefore, we conclude that states should exercise caution while considering legalization of recreational use of marijuana. My second project, “Effects of Social Media Fights and New Product Launches in the Fast Food Industry” examines the effects of engaging in ‘Twitter feuds’ with competition during new product launches. We propose a viable mechanism that explains how seemingly harmless banter of social media could have unforeseen impact on a firm’s business. Through empirical evidence from recent incidents, we show that Twitter activity has a spillover into traditional media which leads to surge in online search. Online search activity is followed by the offline sales as documented in literature as well as evidenced from our unique foot traffic data. Next, we document the long-term effects of this menu innovation in causal framework, well beyond the initial frenzy, with a novel synthetic difference-in-differences (SDID) method proposed by Arkhangelsky et al. (2021). Results show that the launch led to a 30% increase in store visits up to six months after the launch. Overall, these findings underscore the importance of savvy social media presence especially during a product launch- which could be driver for peaked interest leading to impact on overall business. The flip side for competitors is that initiating seemingly harmless banter, unlike in the offline setting, could end up providing free publicity to one’s rivals. Overall, we highlight the enormous potential of social media to affect business and advise caution to brand managers before engaging in any activity. My third project “A study of wear out and heterogeneous effects of unlimited shipping program on customer engagement in the online retail industry” we study effects of a variation of free shipping promotion in the online retail industry. Free shipping promotions have become popular among online retailers. Most online shoppers expect deliveries without additional costs and cite it as a primary concern while shopping online. Many online retailers across industries have implemented long term free shipping programs on all purchases with fixed annual fees. In this paper, we analyze benefits associated with such programs for the retailers and also shed light on the potential pitfalls, using data from a leading online retailer in the UK. Our results indicate that that there is a significant decay in customer spending after initial days and the effects wear out completely short way through the promotion period. Moreover, changes in purchase behavior (significantly lower basket size after enrolling for free shipping) could hurt the retailer. Thus, online retailers should be cautious when offering long term free shipping promotion. In the next part of the paper, we use pre-promotion engagement as a moderating factor to capture heterogeneous effects of free shipping programs across customers, using Honest Causal Forests approach. Our results show that free shipping promotions work better (higher revenues, smaller drop in basket size) for customers with relatively lower engagement with the retailer in the prepromotion period. Online retailers could use these findings to devise their targeting strategy for free shipping promotions.
Author: Neng-Chieh Chang
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Languages : en
Pages : 134
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This dissertation consists of three chapters that study causal inference when applying machinelearning methods. In Chapter 1, I propose an orthogonal extension of the semiparametric difference-in-differences estimator proposed in Abadie (2005). The proposed estimator enjoys the so-called Neyman-orthogonality (Chernozhukov et al. 2018) and thus it allows researchers to flexibly use a rich set of machine learning (ML) methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional data set when the number of potential control variables is larger than the sample size and the conventional nonparametric estimation methods, such as kernel and sieve estimators, do not apply. I apply this orthogonal difference-in-differences estimator to evaluate the effect of tariff reduction on corruption. The empirical results show that tariff reduction decreases corruption in large magnitude. In Chapter 2, I study the estimation and inference of the mode treatment effect. Mean,median, and mode are three essential measures of the centrality of probability distributions. In program evaluation, the average treatment effect (mean) and the quantile treatment effect (median) have been intensively studied in the past decades. The mode treatment effect, however, has long been neglected in program evaluation. This paper fills the gap by discussing both the estimation and inference of the mode treatment effect. I propose both traditional kernel and machine learning methods to estimate the mode treatment effect. I also derive the asymptotic properties of the proposed estimators and find that both estimators follow the asymptotic normality but with the rate of convergence slower than the regular rate N^1/2, which is different from the rates of the classical average and quantile treatment effect estimators. In Chapter 3 (joint with Liqiang Shi), we study the estimation and inference of the doublyrobust extension of the semiparametric quantile treatment effect estimation discussed in Firpo (2007). This proposed estimator allows researchers to use a rich set of machine learning methods in the first-step estimation, while still obtaining valid inferences. Researchers can include as many control variables as they consider necessary, without worrying about the over-fitting problem which frequently happens in the traditional estimation methods. This paper complements Belloni et al. (2017), which provided a very general framework to discuss the estimation and inference of many different treatment effects when researchers apply machine learning methods.
Author: Yoshiyasu Rai
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Languages : en
Pages : 112
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In Chapter 1, I study the statistical inference problem for treatment assignment policies. In typical applications, individuals with different characteristics are expected to differ in their responses to treatment. As a result, treatment assignment policies that allocate treatment based on individuals' observed characteristics can have a significant influence on outcomes and welfare. A growing literature proposes various approaches to estimating the welfare-optimzing treatment assignment policy. I develop a method for assessing the precision of estimated optimal policies. In particular, for the welfare used by \cite{KT:18} to propose estimated assignment policy, my method constructs (i) a confidence set of policies that contains the optimal policy, which maximizes the average social welfare among all the feasible policies with prespecified level and (ii) a confidence interval for the maximized welfare. A simulation study indicates that the proposed methods work reasonably well with modest sample size. I apply the method to experimental data from the National Job Training Partnership Act study. In Chapter 2, I derive the large sample properties of $M$th nearest neighbor propensity score matching estimator with a potentially misspecified propensity score model. By using the local misspecification framework, I formalize the bias/variance trade-off with respect to the choice of propensity score estimator and propose a model selection criterion that aims to minimize the estimation error. Finally, in Chapter 3 (co-authored with Taisuke Otsu), we propose asymptotically valid inference methods for matching estimators based on the weighted bootstrap. The key is to construct bootstrap counterparts by resampling based on certain linear forms of the estimators. Our weighted bootstrap is applicable for the matching estimators of both the average treatment effect and its counterpart for the treated population. Also, by incorporating a bias correction method in \cite{AI:11}, our method can be asymptotically valid even for matching based on a vector of covariates. A simulation study indicates that the weighted bootstrap method is favorably comparable with the asymptotic normal approximation by \cite{AI:06}. As an empirical illustration, we apply the proposed method to the National Supported Work data.
Author: Elias Moor
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Languages : en
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Author: Delia Ruth Grigg Bailey
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Category : Electronic dissertations
Languages : en
Pages : 178
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Author: Avi Isaac Feller
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Languages : en
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This dissertation addresses statistical methods for understanding treatment effect variation in randomized experiments, both in terms of variation across pre-treatment covariates and variation across post-randomization intermediate outcomes. These methods are then applied to data from the National Head Start Impact Study (HSIS), a large-scale randomized evaluation of the Federally funded preschool program, which has become an important part of the policy debate in early childhood education.
Author: Patrick F. Burauel
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Languages : en
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