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采用因果推理和贝叶斯建模的理由
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采用因果推理的理由非常简单且令人信服。MMM 估计的所有数量都暗示了因果关系。在估算投资回报率、绘制响应曲线和进行最佳预算分析时，我们会考虑，如果采用不同的营销支出，结果会如何变化，因此整个过程中会考虑营销支出对 KPI 的影响。从 Meridian 设计的角度来看，除了使用因果推理方法之外，别无选择。
Meridian 是一种回归模型。营销效应之所以可被解释为因果影响，是由于所定义的被估量和做出的假设（如因果 DAG）。虽然这些假设可能并不适用于所有广告客户，但我们会公开披露这些假设，以供各个广告客户自行决定。
虽然贝叶斯建模不是因果推理的必要条件，但由于贝叶斯方法具有以下优势，因此被 Meridian 所采用：
- 贝叶斯模型的先验分布提供了一种直观的方式，可根据先验知识和所选正则化强度，对每个形参的拟合进行正则化处理。在 MMM 中，正则化是必要的，因为变量数量很大，相关性通常很高，并且媒体效应（包括 Adstock 和回报递减）非常复杂。
- 就投资回报率而言，Meridian 提供了相关选项，可对回归模型进行重新参数化，从而允许使用任何自定义投资回报率先验。包括实验结果在内的任何及所有可用知识都可以用于设置先验，让您能够以您认为合适的强度对您信任的结果进行正则化处理。
- 媒体变量转换（Adstock 和回报递减）是非线性的，并且这些转换的形参无法通过线性混合模型技术进行估计。但 Meridian 可使用先进的 MCMC 采样技术解决此问题。
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-08-04。
[null,null,["最后更新时间 (UTC)：2025-08-04。"],[[["\u003cp\u003eMeridian adopts a causal inference perspective to measure the true impact of marketing spending on key performance indicators (KPIs) such as ROI, response curves, and optimal budget allocation.\u003c/p\u003e\n"],["\u003cp\u003eBuilt as a Bayesian regression model, Meridian leverages causal assumptions and transparently discloses them, allowing advertisers to assess their applicability.\u003c/p\u003e\n"],["\u003cp\u003eThe Bayesian approach in Meridian provides robust regularization, incorporates prior knowledge about ROI, and effectively handles non-linear media effects through advanced sampling techniques.\u003c/p\u003e\n"]]],["Meridian uses causal inference methodology because MMM estimates imply causality, analyzing how marketing spend affects KPIs. This regression model defines estimands and makes assumptions, which are disclosed for transparency. It employs a Bayesian approach for regularization via prior distributions, reparameterization using ROI priors, and handling nonlinear media variable transformations like adstock and diminishing returns through MCMC sampling techniques. These techniques are needed due to high variable counts and complex media effects.\n"],null,["# Rationale for causal inference and Bayesian modeling\n\nThe reason for taking a causal inference perspective is straightforward and\ncompelling. All of the quantities that MMM estimates imply causality. ROI,\nresponse curves, and optimal budget analysis pertain to how marketing spending\naffects KPIs, by considering what would have happened if the marketing spend had\nbeen different. The Meridian design perspective is that there is no alternative\nbut to use causal inference methodology.\n\nMeridian is a regression model. The fact that marketing effects can be\ninterpreted as causal is owed to the estimands defined and the assumptions made\n(such as the causal DAG). Although these assumptions might not hold for every\nadvertiser, the assumptions are transparently disclosed for each advertiser to\ndecide.\n\nAlthough Bayesian modeling is not necessary for causal inference,\nMeridian takes a Bayesian approach because it offers the following\nadvantages:\n\n1. The prior distributions of a Bayesian model offer an intuitive way to regularize the fit of each parameter according to prior knowledge and the selected regularization strength. Regularization is necessary in MMM because the number of variables is large, the correlations are often high, and the media effects (with adstock and diminishing returns) are complex.\n2. Meridian offers the option to reparameterize the regression model in terms of ROI, allowing the use of any custom ROI prior. Any and all available knowledge, including experiment results, can be used to set priors that regularize towards results you believe in with the strength you believe is appropriate.\n3. Media variable transformations (adstock and diminishing returns) are nonlinear, and the parameters of these transformations cannot be estimated by linear mixed model techniques. Meridian uses state-of-the-art [MCMC sampling\n techniques](/meridian/docs/basics/bayesian-inference#mcmc-convergence) to solve this problem."]]
