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The Cashback Use Case: A Complex Problem in Causal Inference
In the domain of cashback offers, one of the most critical questions is whether offering cashback genuinely increases customer spending or engagement. The challenge here is that the data often comes with confounding variables, such as previous spending habits or income levels. These variables can create a bias, making it difficult to isolate the effect of the cashback offer itself.
Data Structure
– Treatment Group: Customers who availed of the cashback offer.
– Control Group: Customers who did not avail of the cashback offer.
– Covariates: Features like previous spending habits, income level, frequency of transactions, etc.
– Outcome Variable: Customer spending or engagement level.
Traditional Machine Learning Limitations
Traditional machine learning models are excellent at finding correlations but not causation. For example, a regression model might find that customers who avail of cashback offers tend to spend more. However, this doesn’t mean the cashback offer caused the increased spending. It could be that customers who were already inclined to spend more are the ones who take advantage of the cashback offers.
Confounding Variables
In the cashback offers scenario, several confounding variables can affect the outcome, such as the customer’s income level, previous spending habits, or even the time of the year. Traditional ML models generally do not have built-in mechanisms to control for these confounding variables, which can lead to biased or misleading results.
Generalization to New Data
Traditional models often require retraining when applied to new data or different merchant categories. This is not efficient and can be time-consuming, especially when you want to generalize findings across multiple merchants within the same merchant category or across multiple merchant categories.
Inability to Isolate Impact
Traditional machine learning models can’t isolate the impact of the cashback offer from other potential influencing factors. For instance, if a customer spends more during a holiday season and also avails a cashback offer, a traditional model might incorrectly attribute the increased spending solely to the cashback offer.
Lack of Counterfactual Analysis
Traditional models do not provide a way to answer “what-if” questions. For example, “What would have happened to customer spending if the cashback offer was not available?” This is crucial for understanding the true impact of the cashback offer, and traditional ML models fall short here.
Why These Limitations Matter
For merchants, these limitations could mean the difference between running a successful campaign that genuinely increases customer spending and running one that simply attracts customers who would have spent money anyway. For issuers, it could mean the difference between creating an offer that genuinely increases card usage and one that does not.
By understanding these limitations, both merchants and issuers can better appreciate the need for more advanced techniques like Causal Inference with Attention (CInA) for making more informed decisions.
Importance of Causal Inference for Cashback Offers
Merchants are interested in maximizing the return on investment (ROI) for their cashback offers. The primary metric of interest is usually increased customer spending. Issuers are interested in maximizing card usage and overall customer engagement.
Both merchants and issuers face the challenge of confounding variables — factors like income level, previous spending habits, and more — that can cloud the true impact of cashback offers. Traditional machine learning models can provide predictive insights but fail to establish causality.
Why Causal Inference?
Causal inference goes beyond correlation to establish a cause-and-effect relationship. It isolates the impact of the treatment (cashback offer) from other confounding variables, providing a more accurate measure of its effectiveness.
For Merchants ROI Measurement: By isolating the causal impact of cashback offers, merchants can accurately measure their ROI. This enables data-driven decision-making for future campaigns.
Customer Segmentation: Understanding which variables actually influence increased spending allows for more targeted marketing, thus maximizing ROI.
For Issuers Card Usage: By understanding the causal relationship between cashback offers and card usage, issuers can design more effective engagement strategies.
Customer Lifetime Value: Accurate causal inference can help in predicting the long-term engagement levels of customers, which is crucial for calculating Customer Lifetime Value (CLV).
Technical Solution for Cashback Offers: Applying Causal Inference with Attention (CInA)
Understanding the causal impact of cashback offers on customer spending and engagement is a complex problem that traditional machine learning models often fail to address adequately. The paper “Towards Causal Foundation Model: on Duality between Causal Inference and Attention” by Zhang et al. (2023) introduces Causal Inference with Attention (CInA) as a novel method to tackle such causal inference problems. The paper establishes that there is an equivalence between optimal covariate balancing and self-attention mechanisms in transformer models. This primal-dual connection serves as the theoretical backbone for the CInA method, enabling zero-shot causal inference on new, unseen data (Zhang et al., 2023).
Step 1: Self-Supervised Learning for Covariate Balancing
In the context of cashback offers, the first step involves using self-supervised learning to balance covariates like previous spending habits and income levels between the treatment group (those who availed the cashback) and the control group (those who did not). The attention mechanism in a transformer model is employed to weigh these covariates, effectively balancing them across both groups. The attention mechanism is mathematically formulated as a weighted sum of these covariates. The weights are learned to minimize the difference in covariate distributions between the two groups, as established in the paper (Zhang et al., 2023).
Step 2: Zero-Shot Causal Inference
Once the model is trained to balance the covariates, it is capable of performing zero-shot causal inference. This means the model can be applied to different sets of data from various merchants or issuers without requiring retraining. The model uses key-value pairs extracted from the last layer of the transformer to make causal inferences. The keys represent the balanced covariates, and the values signify the potential outcomes, such as spending levels (Zhang et al., 2023).
Step 3: Empirical Validation
The final step involves empirically validating the model’s causal inferences using real-world data. This could involve methods like A/B testing or statistical techniques like propensity score matching. Metrics like the Average Treatment Effect (ATE) could be used to quantify the causal impact of cashback offers on customer spending. By applying the principles of CInA, organizations can offer a robust, data-driven answer to the critical question: “Do our cashback offers actually work?” This is invaluable for optimizing offers, improving relationships with merchants and issuers, and enhancing overall customer engagement.
Source Citation
Zhang, J., Jennings, J., Zhang, C., & Ma, C. (2023). Towards Causal Foundation Model: on Duality between Causal Inference and Attention. Microsoft Research Cambridge; Massachusetts Institute of Technology. September 29, 2023.