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Partial Identification of Discrete Counterfactual Distributions with Sequential Update of Information

机译:具有信息顺序更新的离散反事实分布的部分识别

摘要

The credibility of standard instrumental variables assumptions is often under dispute. This paper imposes weak monotonicity in order to gain information on counterfactual outcomes, but avoids independence or exclusion restrictions. The outcome process is assumed to be sequentially ordered, building up and depending on the information level of agents. The potential outcome distribution is assumed to weakly increase (or decrease) with the instrument, conditional on the continuation up to a certain stage. As a general result, the counterfactual distributions can only be bounded, but the derived bounds are informative compared to the no-assumptions bounds thus justifying the instrumental variables terminology. The construction of bounds is illustrated in two data examples.
机译:标准工具变量假设的可信度经常引起争议。本文强加弱单调性以获取有关反事实结果的信息,但避免了独立性或排除性限制。假定结果过程是按顺序排序,建立的,并取决于代理的信息级别。假设在持续到某个阶段的情况下,潜在的结果分布会随着工具的增加而微弱地增加(或减少)。一般结果是,反事实分布只能是有界的,但与无假设界相比,得出的界是有益的,因此证明了工具变量术语的合理性。在两个数据示例中说明了边界的构造。

著录项

  • 作者

    Boes, Stefan;

  • 作者单位
  • 年度 2009
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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