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A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.
In this paper, we estimate the causal effect of political power on the provision of public education. We use data from a historical nondemocratic society with a weighted voting system where eligible voters received votes in proportion to their taxabl
This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reput
A majority portion of the slum people is involved in service sectors. The city dwellers are somehow dependent on the services of those people. Pure drinking water and hygiene is a significant concern in the slums. Because of the lack of these two ite
How does food consumption improve educational outcomes is an important policy issue for developing countries. Applying the Indonesian Family Life Survey (IFLS) 2014, we estimate the returns of food consumption to education and investigate if more edu
The study examines the relationship between mobile financial services and individual financial behavior in India wherein a sizeable population is yet to be financially included. Addressing the endogeneity associated with the use of mobile financial s