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需要的假设
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根据您的偏好保存内容并对其进行分类。
一般来说,回归中没有潜在结果这个概念,因为回归模型估计的是响应变量的条件预期。但是,在条件可交换性和一致性的关键假设下:
$$
E \Biggl(
\overset \sim Y_{g,t}^{
\left(\left\{
x_{g,t,i}^{(\ast)}
\right\}\right)
} \Big| \bigl\{z_{g,t,i}\bigr\}
\Biggr) = E \Biggl(
\overset \sim Y_{g,t} \Big|
\bigl\{z_{g,t,i}\bigr\}, \big\{x_{g,t,i}^{(\ast)}\bigr\} \Biggr)
$$
关键假设
条件可交换性:
\( \overset \sim Y_{g,t}^{(\{ x_{g,t,i}^{(\ast)} \})} \)与任何反事实情景\(\bigl\{ x_{g,t,i}^{(\ast)} \bigr\}\)的随机变量\(\bigl\{ X_{g,t,i}^{(\ast)} \bigr\}\) 无关。因此,潜在结果集有条件地独立于广告客户的历史媒体执行决策。
一致性:
当 \(\bigl\{ X_{g,t,i}^{(\ast)} \bigr\} =
\bigl\{ x_{g,t,i}^{(\ast)} \bigr\}\)时,\( \overset \sim Y_{g,t} = \overset \sim Y_{g,t}^{
(\{ x_{g,t,i}^{(\ast)} \})
} \) 。因此,观测到的 KPI 实现了与广告客户历史媒体执行情况相匹配的反事实情景的潜在结果。
根据这些假设,就会得出前面所说的结果:
$$
E \Biggl( \overset \sim Y_{g,t}^{
\left(\left\{ x_{g,t,i}^{\ast} \right\}\right)
} \Big| \bigl\{ z_{g,t,i} \bigr\} \Biggr)
\overset{\text{exchangeability}}{=} E \Biggl( \overset \sim Y_{g,t}^{
\left(\left\{ x_{g,t,i}^{\ast} \right\}\right)
} \Big| \bigl\{ z_{g,t,i} \bigr\},\ \bigl\{ x_{g,t,i}^{(\ast)} \bigr\} \Biggr)
\overset{\text{consistency}}{=} E \Biggl( \overset \sim Y_{g,t}\ \Big|
\bigl\{ z_{g,t,i} \bigr\},\ \bigl\{ x_{g,t,i}^{(\ast)} \bigr\}
\Biggr)
$$
一致性假设相当直观,除非反事实定义不清或未在数据中准确体现,否则该假设成立。如需了解详情,请参阅 Hernan MA、Robins JM,(2020 年)Causal Inference: What If。
条件可交换性假设就不那么直观了。如果所有混杂变量都获测量并包含在控制变量数组 \(\{z_{g,t,i}\}\)中,则此假设成立。混杂变量是指对观测到的实验组 \(\{x_{g,t,i}\}\) 和结果组\(\{\overset \sim y_{g,t}\}\)都有因果效应的任何变量。对实验组的因果效应可以是广告客户整体预算水平的效应,也可以是跨渠道分配、跨地理位置分配或跨时间段分配的效应。在实践中,我们很难知道是否所有的混杂变量都得到了测量,因为这纯粹是一种假设,而且没有统计检验方法可以根据数据确定这一点。不过,如果假设因果图符合一个被称为“后门标准”(Pearl, J.,2009 年)的条件,那么条件可交换性假设就会成立,意识到这一点非常有帮助。如需了解详情,请参阅因果图。
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最后更新时间 (UTC):2025-08-04。
[null,null,["最后更新时间 (UTC):2025-08-04。"],[[["\u003cp\u003eRegression models can be used to estimate potential outcomes under the assumptions of conditional exchangeability and consistency.\u003c/p\u003e\n"],["\u003cp\u003eConditional exchangeability implies that potential outcomes are independent of historical media execution decisions, given confounding variables.\u003c/p\u003e\n"],["\u003cp\u003eConsistency means the observed outcome matches the potential outcome for the actual historical media execution.\u003c/p\u003e\n"],["\u003cp\u003eConfounding variables, which affect both treatment and outcome, must be measured and included for conditional exchangeability to hold.\u003c/p\u003e\n"],["\u003cp\u003eWhile there's no statistical test to guarantee conditional exchangeability, causal graphs and the backdoor criterion can help assess it.\u003c/p\u003e\n"]]],["Regression models typically lack potential outcomes, but under conditional exchangeability and consistency, we can derive a relevant result. Conditional exchangeability means potential outcomes are independent of historical media execution. Consistency dictates that observed outcomes match potential outcomes when treatment equals historical media execution. The key result is derived by first exchanging outcomes with potential outcomes, then aligning them with observed values under these assumptions. Conditional exchangeability relies on all confounders (variables affecting both treatment and outcome) being measured and can be assessed with causal graph analysis.\n"],null,["# Required assumptions\n\nGenerally speaking, there is no concept of potential outcomes in regression\nbecause regression models estimate conditional expectations of a response\nvariable. However, under the key assumptions of *conditional exchangeability*\nand *consistency*: \n$$ E \\\\Biggl( \\\\overset \\\\sim Y_{g,t}\\^{ \\\\left(\\\\left\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\right\\\\}\\\\right) } \\\\Big\\| \\\\bigl\\\\{z_{g,t,i}\\\\bigr\\\\} \\\\Biggr) = E \\\\Biggl( \\\\overset \\\\sim Y_{g,t} \\\\Big\\| \\\\bigl\\\\{z_{g,t,i}\\\\bigr\\\\}, \\\\big\\\\{x_{g,t,i}\\^{(\\\\ast)}\\\\bigr\\\\} \\\\Biggr) $$\n\n**Key assumptions**\n\n- Conditional exchangeability:\n\n \\\\( \\\\overset \\\\sim Y_{g,t}\\^{(\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\})} \\\\)\n is independent of the random variables\n \\\\(\\\\bigl\\\\{ X_{g,t,i}\\^{(\\\\ast)} \\\\bigr\\\\}\\\\) for any counterfactual scenario\n \\\\(\\\\bigl\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\bigr\\\\}\\\\). So, the set of potential outcomes\n is conditionally independent of the advertiser's historical media execution\n decision.\n- Consistency:\n\n \\\\( \\\\overset \\\\sim Y_{g,t} = \\\\overset \\\\sim Y_{g,t}\\^{\n (\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\})\n } \\\\) when \\\\(\\\\bigl\\\\{ X_{g,t,i}\\^{(\\\\ast)} \\\\bigr\\\\} =\n \\\\bigl\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\bigr\\\\}\\\\). So, the observed KPI realization of\n the potential outcome for the counterfactual scenario matching the\n advertiser's historical media execution.\n\nUnder these assumptions, you have the previously stated result: \n$$ E \\\\Biggl( \\\\overset \\\\sim Y_{g,t}\\^{ \\\\left(\\\\left\\\\{ x_{g,t,i}\\^{\\\\ast} \\\\right\\\\}\\\\right) } \\\\Big\\| \\\\bigl\\\\{ z_{g,t,i} \\\\bigr\\\\} \\\\Biggr) \\\\overset{\\\\text{exchangeability}}{=} E \\\\Biggl( \\\\overset \\\\sim Y_{g,t}\\^{ \\\\left(\\\\left\\\\{ x_{g,t,i}\\^{\\\\ast} \\\\right\\\\}\\\\right) } \\\\Big\\| \\\\bigl\\\\{ z_{g,t,i} \\\\bigr\\\\},\\\\ \\\\bigl\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\bigr\\\\} \\\\Biggr) \\\\overset{\\\\text{consistency}}{=} E \\\\Biggl( \\\\overset \\\\sim Y_{g,t}\\\\ \\\\Big\\| \\\\bigl\\\\{ z_{g,t,i} \\\\bigr\\\\},\\\\ \\\\bigl\\\\{ x_{g,t,i}\\^{(\\\\ast)} \\\\bigr\\\\} \\\\Biggr) $$\n\nThe consistency assumption is fairly intuitive, and holds unless the\ncounterfactual is poorly defined or is not accurately represented in the data.\nFor more information, see [Hernan MA, Robins JM, (2020) Causal Inference: What\nIf](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/).\n\nThe conditional exchangeability assumption is a bit less intuitive. This\nassumption holds if all confounding variables are measured and included in the\ncontrol array \\\\(\\\\{z_{g,t,i}\\\\}\\\\). *Confounding variables* are anything that has\na causal effect on both the observed treatment \\\\(\\\\{x_{g,t,i}\\\\}\\\\) and outcome\n\\\\(\\\\{\\\\overset \\\\sim y_{g,t}\\\\}\\\\). A causal effect on treatment can mean an effect\nof the advertiser's overall budget level, the allocation across channels, the\nallocation across geos, or the allocation across time periods. In practice, it\nis difficult to know whether all of the confounding variables are measured\nbecause it is purely an assumption, and there is no statistical test to\ndetermine this from your data. However, it can be helpful to know that the\nconditional exchangeability assumption holds if you assume a causal graph that\nmeets a condition known as the *backdoor criterion* (Pearl, J., 2009). For more\ninformation, see [Causal graph](/meridian/docs/basics/causal-graph)."]]