建立强可忽略处理分配条件下因果推断的结构回归模型，估计平均处理效应。

A structural regression model for causal inference is established to estimate the average treatment effect under strongly ignorable treatment assignment.

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当没有异质性存在，或处理效应分布是大致对称的时候，它们的答案是一致的。

The answers to these questions coincide either when no heterogeneity is present, or when the distribution of the treatment effects is roughly symmetrical.

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在可交换条件下，当响应变量为多维时，利用结构回归模型研究总体平均处理效应的估计。

In this paper, on the basis of structural regression models the estimation of the population-averaged treatment effects is given when the response variable is a multi-dimensional vector.

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Non-obvious ideas were conceived of to solve these problems of long-term risks and to get around the psychological barriers imposed by framing biases and psychological biases, in order to allow people to actually manage the risk and to get around moral hazard.

解决方法需要被研究并发掘,来处理长期风险问题,避开由于框架效应,引起的心理障碍，能够使人们真正控制风险,规避道德风险。

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