Machine Learning Under Endogeneity

Recent advances in machine learning literature provide a series of new algorithms that both address endogeneity and can be applied in high-dimensional environments, we call them MLIV. These algorithms are data-driven and exploit various forms of regularization to ameliorate the ill-posedness of the problem while maintaining the functional form flexibility. In this thesis, we discuss how MLIV estimators can be used to answer economic questions. In the first chapter, Causal Gradient Boosting: Boosted Instrumental Variables Regression, we propose an MLIV algorithm called boostIV that builds on the traditional gradient boosting algorithm and corrects for the endogeneity bias. The algorithm is very intuitive and resembles an iterative version of the standard 2SLS estimator. The second chapter, Automatic Debiased Machine Learning in Presence of Endogeneity, introduces an approach for performing valid asymptotic inference on regular functionals of MLIV estimators. The approach is based on construction of an orthogonal moment function that has a zero derivative with respect to the MLIV estimator. We develop a penalized GMM estimator of the bias correction term necessary to obtain asymptotically normal debiased estimates and derive its convergence rate. We also give conditions for root-n consistency and asymptotic normality of the debiased MLIV estimator of the functional of interest. Finally, in the third chapter, Flexible Demand Estimation using Machine Learning, we demonstrate how to estimate substitution patterns in the market for sodas using the debiasing procedure from the second chapter. These three chapters are highly interconnected. The first chapter proposes a new MLIV algorithm for flexible estimation in presence of endogenous regressors. However, it focuses on the underlying structural function which in the majority of cases does not have a clear economic interpretation. While the second chapter develops a method to perform inference on functionals of MLIV estimators, which have a clear economic interpretation and can be used to answer various economic questions of interest. Finally, the third chapter investigates an important applied question of flexible estimation of demand for differentiated goods, which is a perfect example of a high-dimensional problem with endogenous regressors. As a result, we get a full picture about the potential of MLIV methods in economics

Machine Learning under Endogeneity

Recent advances in machine learning literature provide a series of new algorithms that both address endogeneity and can be applied in high-dimensional environments, we call them MLIV. These algorithms are data-driven and exploit various forms of regularization to ameliorate the ill-posedness of the problem while maintaining the functional form flexibility. In this thesis, we discuss how MLIV estimators can be used to answer economic questions. In the first chapter, Causal Gradient Boosting: Boosted Instrumental Variables Regression, we propose an MLIV algorithm called boostIV that builds on the traditional gradient boosting algorithm and corrects for the endogeneity bias. The algorithm is very intuitive and resembles an iterative version of the standard 2SLS estimator. The second chapter, Automatic Debiased Machine Learning in Presence of Endogeneity, introduces an approach for performing valid asymptotic inference on regular functionals of MLIV estimators. The approach is based on construction of an orthogonal moment function that has a zero derivative with respect to the MLIV estimator. We develop a penalized GMM estimator of the bias correction term necessary to obtain asymptotically normal debiased estimates and derive its convergence rate. We also give conditions for root-n consistency and asymptotic normality of the debiased MLIV estimator of the functional of interest. Finally, in the third chapter, Flexible Demand Estimation using Machine Learning, we demonstrate how to estimate substitution patterns in the market for sodas using the debiasing procedure from the second chapter. These three chapters are highly interconnected. The first chapter proposes a new MLIV algorithm for flexible estimation in presence of endogenous regressors. However, it focuses on the underlying structural function which in the majority of cases does not have a clear economic interpretation. While the second chapter develops a method to perform inference on functionals of MLIV estimators, which have a clear economic interpretation and can be used to answer various economic questions of interest. Finally, the third chapter investigates an important applied question of flexible estimation of demand for differentiated goods, which is a perfect example of a high-dimensional problem with endogenous regressors. As a result, we get a full picture about the potential of MLIV methods in economics

Efficiency analysis of 30-stage fracturing in a horizontal well to oil rims based on through-barrier diagnostics

© 2019, Society of Petroleum Engineers Cost-effective reservoir development of oil rims such as Novoportovskoe field requires improving of existing fracturing methods on a way to increase recovery factor. One of the approaches includes the implementation of special completion with renewable fracturing sleeves as well as increasing the number of fracture ports over horizontal wellbore (Belov and others, 2018). This allows to increase profitability from one side, but from the other side, considering the complexity of oil rims geology, this increases the risks of early gas and water breakthrough from the gas cap and water contact respectively via hydraulic fractures induced to untargeted intervals. Generally, the efficiency of implemented well completion design is evaluated based on conventional production logging, however, high resolution and sensitive well diagnostics are required in case of a high number of fracture ports. An additional challenge is associated with diagnostics of flow in the reservoir behind the liner and completion components integrity evaluation such as packer and fracture sleeves. The better diagnostics is applied the more efficient production profile optimization can be done. The paper describes the through-barrier diagnostics concept and shows a case of the performed survey in a horizontal well to oil rim with 30-stage hydraulic fracturing based on through-barrier diagnostics. Two surveys were conducted: the first survey was done at an early stage of well production, then workover was performed based on the first survey results revealing several zones with high GOR and high water cut. The second survey was conducted after the workover. Additionally, the paper describes a novel method on high GOR and high WC zones identification from spectral acoustic data analysis based on machine learning

Impact of vaccination against the novel coronavirus infection (COVID-19) with Sputnik V on mortality during the delta variant surge

Objectives: The aim is to study impact of vaccination against the novel coronavirus disease (COVID-19) with Sputnik V on mortality during the period of predominance of the delta variant of SARS-CoV-2. Methods: This was a retrospective cohort study of individuals with state health insurance at the Moscow Ambulatory Center. The cohorts included 41,444 persons vaccinated with Sputnik V, 15,566 survivors of COVID-19, and 71,377 non-immune persons. The deaths of patients that occurred from June 1, 2021, to August 31, 2021, were analyzed. Results: Overall (0.39 % vs. 1.92 %; p  85 years. Conclusion: COVID-19 vaccination with Sputnik V is associated with a decrease in overall and COVID-19-related mortality and is not with increased non-COVID mortality

Gut Microbiota and Biomarkers of Endothelial Dysfunction in Cirrhosis

Our aim was to study the association of endothelial dysfunction biomarkers with cirrhosis manifestations, bacterial translocation, and gut microbiota taxa. The fecal microbiome was assessed using 16S rRNA gene sequencing. Plasma levels of nitrite, big endothelin-1, asymmetric dimethylarginine (ADMA), presepsin, and claudin were measured as biomarkers of endothelial dysfunction, bacterial translocation, and intestinal barrier dysfunction. An echocardiography with simultaneous determination of blood pressure and heart rate was performed to evaluate hemodynamic parameters. Presepsin, claudin 3, nitrite, and ADMA levels were higher in cirrhosis patients than in controls. Elevated nitrite levels were associated with high levels of presepsin and claudin 3, the development of hemodynamic circulation, hypoalbuminemia, grade 2–3 ascites, overt hepatic encephalopathy, high mean pulmonary artery pressure, increased abundance of Proteobacteria and Erysipelatoclostridium, and decreased abundance of Oscillospiraceae, Subdoligranulum, Rikenellaceae, Acidaminococcaceae, Christensenellaceae, and Anaerovoracaceae. Elevated ADMA levels were associated with higher Child–Pugh scores, lower serum sodium levels, hypoalbuminemia, grade 2–3 ascites, milder esophageal varices, overt hepatic encephalopathy, lower mean pulmonary artery pressure, and low abundance of Erysipelotrichia and Erysipelatoclostridiaceae. High big endothelin-1 levels were associated with high levels of presepsin and sodium, low levels of fibrinogen and cholesterol, hypocoagulation, increased Bilophila and Coprobacillus abundances, and decreased Alloprevotella abundance