&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

We use a debiased Machine Learning technique to explore causes behind infant malnutrition for households below-poverty-line in India and examine effectiveness of various government interventions along with other factors. Our analysis reveals that access to clean water is one of the most crucial issues to focus on. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

School of Artificial Intelligence and Data Science (AIDE)

World University root node

Applied Economics Letters

Infant malnutrition, clean-water access and government interventions in India: a machine learning approach towards causal inference

This article uses a nation-wide household survey data from India and identifies important predictors of neonatal and infant mortality using multiple machine learning (ML) techniques. The consensus on the leading predictors from the interpretable ML algorithms (that we use) serve as early warning signs of neonatal and infant mortality. This enables us to identify a ‘high-mortality risk’ group of mothers and infants–an important goal of India’s ‘India Newborn Action Plan’. This high-risk group comprises firstborns, mothers with prior deaths or several previous births, newborns suffering from complicated deliveries, small size at birth and unvaccinated infants. We identify early newborn care, folic acid supplements and conditional cash transfer (Janani Suraksha Yojana) as the most effective policy interventions. Given the imbalanced nature of the dependent variable (‘events’ being rarer than ‘non-events’) we use additional ML methods (along with the commonly used ones) that are tailor-made for ‘rare-event’ prediction for robustness checks. We also use an evaluation measure called Area under Precision Recall Curve that is tailored for measuring prediction accuracy with imbalanced data. Our analysis sheds light on policy relevance and suggests some new policy prescriptions such as close monitoring of at-risks babies including females and those with small birth-size. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Applied Economics

Early warning signs: targeting neonatal and infant mortality using machine learning

The role of two policy pillars in achieving immunisation coverage of essential vaccines in India is examined by assessing the funds disbursed by the centre to the state governments under the Universal Immunisation Programme and the available physical health infrastructure in the states. It is found that funds by themselves are ineffective in improving vaccination coverage but are effective in conjunction with the available infrastructure. This finding has important implications for achieving full immunisation coverage, which stems from the sharing of responsibilities between the central and state governments under India's federalised system of government. © 2020 Economic and Political Weekly. All rights reserved.

Economic and Political Weekly

Policy complementarities in achieving universal immunisation coverage in India

This paper examines the predictors of immunization coverages of children across Indian states and evaluates the role of Universal Immunization Program (UIP) - a comprehensive policy of the Indian government in that light. Employing Machine Learning methods such as LASSO and hierarchical LASSO, we find that not the UIP expenditure by itself, but health infrastructure turns out to be a robust predictor of immunization coverage. The policy prescription that follows from our study is that the immunization program should focus on promoting the required health infrastructure in addition to monitoring the usage of funds closely for facilitating effective usage of the money. We also scrutinize performances of `BIMARU', states that are considered traditionally underperforming states in terms of health and education.

Economics Bulletin

India's Universal Immunization Program: a lesson from Machine Learning

Essays in the Application of Machine Learning in Development Economics

Using state-level data on India's MidDay Meal program (school lunch program for children) we scrutinize if funds disbursed and food-grains supplied for the purpose can actually serve as good determinants of number of children covered (fed) by the scheme (our dependent variable). Our standard regression studies find that the effect of food-grain supplied has statistically significant effect on the dependent variable while the marginal effects of funds/money disbursed is not statistically significant. Using LASSO based Machine Learning techniques and after controlling for several correlated variables and state level factors we find that both policy variables-funds and food-grains however act as good out-of-sample predictors of number of children being covered. Evaluation of out-of-sample prediction performance of regression models and covariates is a very important but mostly an uncharted territory in development economics. Note that funds are used partly to provide fixed costs of the food service provided and therefore it is not surprising that the coefficient estimate of this covariate (i.e., its marginal effect on dependent variable) does not appear to be statistically significant; but it can still act as a good overall predictor for our dependent variable.