Nonparametric identification strategy is employed to capture causal relationships without imposing any variant of monotonicity existing in the nonseparable nonlinear error model literature. Monotonicity may fail to exist for fundamental reasons related e.g., to the strategic behavior of economic...

The current paper discusses approximating a correct theory of cause and effect by minimizing distance to its associated probability measure in a space of measures in which each element is associated with a stochastic representation of a candidate theory. The discussion encourages researchers to...

In the age of global capitalism, substantial amounts of capital are flowing from developed economies to emerging economies like India. An important feature of the development of Indian stock market has been the escalating participation of Foreign Institutional Investors (FIIs) in the last 15...

There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical...

This study investigates the dynamic relationship between energy consumption and economic growth in nine South and Southeast Asian countries (i.e., Bangladesh, Brunei Darussalam, India, Indonesia, Malaysia, Pakistan, the Philippines, Sri Lanka, and Thailand) using a panel data framework. The...

This study examines the relationship between crude oil prices, US dollar exchange rates and thirty selected international agricultural prices and five international fertilizer prices in a panel framework. The study uses panel VAR methods and Granger causality tests on panel data sets of...

The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential...

Causal knowledge is critical for strategic and organizational decision-making. By contrast, standard machine learning approaches remain purely correlational and prediction-based, rendering them unsuitable for addressing a wide variety of managerial decision problems. Taking a mixed-methods...

This essay reviews progress in empirical economics since Leamer's (1983) critique. Leamer highlighted the benefits of sensitivity analysis, a procedure in which researchers show how their results change with changes in specification or functional form. Sensitivity analysis has had a salutary but...