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Required assumptions
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Generally speaking, there is no concept of potential outcomes in regression
because regression models estimate conditional expectations of a response
variable. However, under the key assumptions of conditional exchangeability
and consistency:
$$
E \Biggl(
\overset \sim Y_{g,t}^{
\left(\left\{
x_{g,t,i}^{(\ast)}
\right\}\right)
} \Big| \bigl\{z_{g,t,i}\bigr\}
\Biggr) = E \Biggl(
\overset \sim Y_{g,t} \Big|
\bigl\{z_{g,t,i}\bigr\}, \big\{x_{g,t,i}^{(\ast)}\bigr\} \Biggr)
$$
Key assumptions
Conditional exchangeability:
\( \overset \sim Y_{g,t}^{(\{ x_{g,t,i}^{(\ast)} \})} \)
is independent of the random variables
\(\bigl\{ X_{g,t,i}^{(\ast)} \bigr\}\) for any counterfactual scenario
\(\bigl\{ x_{g,t,i}^{(\ast)} \bigr\}\). So, the set of potential outcomes
is conditionally independent of the advertiser's historical media execution
decision.
Consistency:
\( \overset \sim Y_{g,t} = \overset \sim Y_{g,t}^{
(\{ x_{g,t,i}^{(\ast)} \})
} \) when \(\bigl\{ X_{g,t,i}^{(\ast)} \bigr\} =
\bigl\{ x_{g,t,i}^{(\ast)} \bigr\}\). So, the observed KPI realization of
the potential outcome for the counterfactual scenario matching the
advertiser's historical media execution.
Under these assumptions, you have the previously stated result:
$$
E \Biggl( \overset \sim Y_{g,t}^{
\left(\left\{ x_{g,t,i}^{\ast} \right\}\right)
} \Big| \bigl\{ z_{g,t,i} \bigr\} \Biggr)
\overset{\text{exchangeability}}{=} E \Biggl( \overset \sim Y_{g,t}^{
\left(\left\{ x_{g,t,i}^{\ast} \right\}\right)
} \Big| \bigl\{ z_{g,t,i} \bigr\},\ \bigl\{ x_{g,t,i}^{(\ast)} \bigr\} \Biggr)
\overset{\text{consistency}}{=} E \Biggl( \overset \sim Y_{g,t}\ \Big|
\bigl\{ z_{g,t,i} \bigr\},\ \bigl\{ x_{g,t,i}^{(\ast)} \bigr\}
\Biggr)
$$
The consistency assumption is fairly intuitive, and holds unless the
counterfactual is poorly defined or is not accurately represented in the data.
For more information, see Hernan MA, Robins JM, (2020) Causal Inference: What
If.
The conditional exchangeability assumption is a bit less intuitive. This
assumption holds if all confounding variables are measured and included in the
control array \(\{z_{g,t,i}\}\). Confounding variables are anything that has
a causal effect on both the observed treatment \(\{x_{g,t,i}\}\) and outcome
\(\{\overset \sim y_{g,t}\}\). A causal effect on treatment can mean an effect
of the advertiser's overall budget level, the allocation across channels, the
allocation across geos, or the allocation across time periods. In practice, it
is difficult to know whether all of the confounding variables are measured
because it is purely an assumption, and there is no statistical test to
determine this from your data. However, it can be helpful to know that the
conditional exchangeability assumption holds if you assume a causal graph that
meets a condition known as the backdoor criterion (Pearl, J., 2009). For more
information, see Causal graph.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-06-11 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-06-11 UTC."],[[["\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)."]]