This presentation will demonstrate how to produce informative, robust, and complex graphs using reproducible official and community-contributed routines in Stata. We will also discuss commonly used programming tools and tips for creating more engaging graphs.

You can use treatment-effects estimators to draw causal inferences from observational data. You can use lasso when you want to control for many potential covariates. With standard treatment-effects models, there is an intrinsic conflict between two required assumptions. The conditional independence assumption is likely to be satisfied with many variables in the model, while the overlap assumption is likely to be satisfied with fewer variables in the model.

How to solve the negative impact of environmental pollution caused by energy consumption on public health is an important challenge to achieving the goal of a healthy China, and the development of clean energy provides a feasible governance path for this. This presentation takes the commissioning and operation of the West–East Gas Pipeline II Project as a quasinatural experiment and uses the China Health and Nutrition Survey (CHNS) data from 2006 to 2015 to empirically examine how clean energy development affects public health. The study found that the West–East Gas Pipeline Project has produced health effects, and after passing multiple robustness tests, it can still significantly improve the public health level in the areas along the route. However, this effect is mainly reflected in urban residents and the elderly, and the improvement of household energy consumption structure, enterprise pollution reduction, and improvement of urban environmental quality are the main channels of action. Further analysis shows that the “coal to gas” policy helps to enhance the health effects of the project.

To make the conventional synthetic control methods more flexible to estimate the average treatment effect (ATE), this presentation proposes a quasisynthetic control method for nonlinear models under the index model framework with possible high-dimensional covariates. The presentation suggests using the minimum average variance estimation (MAVE) method to estimate parameters and the lasso-type procedure to choose high-dimensional covariates. We derive the asymptotic distribution of the proposed ATE estimators for both finite and diverging dimensions of covariates.

Average causal response function (ACRF) is a useful tool to assess treatment effect with dose functions, especially when the treatment is endogenous. This presentation presents the identification and estimation of an ACRF with sample selection and high-dimensional controls. We derive the Neyman-orthogonal moments with multiple nuisance parameters.

Over the past thirty years, project evaluation econometrics has experienced great development. Thanks to the continuous progress of econometric analysis software technology and the increasing abundance of data, the empirical research paradigm of economics and even the research paradigm of economics as a whole has undergone a huge transformation, which has profoundly affected the teaching and research of economics. In the past five years, mainstream project evaluation econometric methods such as DID, IV, and RD and a number of new econometric theoretical advances have emerged. On the one hand, they have repaired and improved the original theoretical methods and application practices, and on the other hand, they have promoted the further development of project evaluation econometric methods. I will give a general introduction to these advances.

The peer-effect (or neighbor-effect) model is an important model for studying the mutual influence between individuals. Its setting is similar to that of the spatial econometric model. However, in the spatial econometric model, the adjacency matrix is often regarded as exogenous. If the adjacency matrix is not a geographical network, but a social or economic network then the exogeneity assumption is unreasonable. This presentation proposes Stata estimation commands for heterogeneous peer-effect models and endogenous peer-effect models, snreghnet and snregenet. snreghnet can examine the row heterogeneity and column heterogeneity of the model. snregenet calculates the two-stage instrumental-variables estimation of the model and uses wild bootstrapping to calculate the standard error.

As one of the most important quasiexperimental causal inference methods, regression discontinuity design has two major analytical frameworks, which are quite different in terms of premise assumptions, bandwidth selection, and inference methods. Among them, the continuity-based framework assumes that the conditional expectation of the potential results is continuous and is widely used in empirical research. The local randomization framework is a rising star. This framework assumes that the driving variables can be regarded as randomly assigned in a small window near the breakpoint. This presentation will introduce the principles and techniques of these two frameworks, including identification, estimation, and inference, and compare the differences between the two through Monte Carlo simulation and Stata cases, as well as their application prospects.