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用于缺失数据处理的图模型AI 解读

This article reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: transparency, estimability, and testability. We then show how procedures based on graphical models can overcome these limitations and provide meaningful performance guarantees even when data are missing not at random (MNAR). In particular, we identify…

Haavelmo 与因果演算的形成AI 解读

Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome and lays out a logical framework that has evolved from Haavelmo’s insight and matured into a coherent and comprehensive account of the relationships between theory, data, and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex…

对 Neuberg《因果性》书评的评论AI 解读

I am grateful to Professor Neuberg for taking the time to study my book Causality and for summarizing its main ideas in a language familiar to readers of Econometric Theory . I will comment on several issues that I believe warrant further discussion. First, I will summarize in my own style what economists can learn from Causality , and, second, I will correct several inaccuracies in the summary of Professor Neuberg.

不完全依从研究中的处理效应界AI 解读

This article establishes nonparametric formulas that can be used to bound the average treatment effect in experimental studies in which treatment assignment is random but subject compliance is imperfect. The bounds provided are the tightest possible, given the distribution of assignments, treatments, and responses. The formulas show that even with high rates of noncompliance, experimental data can yield useful and sometimes accurate information on the average effect of a…

实证研究中的因果图AI 解读

The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for…