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因果推論和貝葉斯模擬
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
提出因果推論觀點相當簡單明瞭
極具吸引力行銷組合模式分析估算出的因果關係的所有數量，投資報酬率
回應曲線，以及與行銷支出有關的最佳預算分析
影響 KPI，思考如果行銷支出
有些人也不一樣。而 Meridian 的設計觀點
但要使用因果推論方法
子午線是迴歸模型行銷效果可說是
導致估計值與做出的假設
(例如因果關係 DAG)？雖然這些假設不一定每次
我們會清楚公開每位廣告客戶的假設
雖然系統不一定會使用貝氏模型來進行因果推論
梅里迪亞採用貝葉斯做法，因為它提供以下功能
優點：
- 依先前貝葉斯模型分佈情況，提供直觀的方法
依照先前的知識和
所選的正則化強度行銷組合模式分析中必須定期進行
變數數量偏大，相關性通常較高，
媒體效果 (含 adstock 並降低報酬率) 相當複雜，
- 梅里達提供重新參數化模型的選項
，您可以先使用任何自訂投資報酬率，不限和所有
實驗結果等可用的知識可用於設定先驗知識
對你相信的力量進行正規化
沒有問題。
- 媒體變數轉換 (adstock 和減損報酬) 為
非線性，因此無法以下列方式預估這些轉換的參數：
線性混合模型梅里迪安採用最先進的技術
MCMC 取樣
技巧
才能解決這個問題
除非另有註明，否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權，程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間：2024-09-05 (世界標準時間)。
[null,null,["上次更新時間：2024-09-05 (世界標準時間)。"],[[["\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."]]
