1. Introduction

Welcome to this video where we will introduce our unique approach to enable cross-collaboration between data science teams and decision makers. We aim to provide informed and data-driven decision-making solutions that generate actionable recommendations to solve business critical problems. In this video, we will explore why decision optimization packages are essential, what they are, and how your data science teams can deploy these solutions to drive business value.

2. Why Decision Optimization Packages Matter

CoLens has invested heavily in decision optimization packages for three key reasons. First, these packages allow decision makers to generate accurate and actionable recommendations, leveraging the power of Corsal AI and structural causal models. Second, they enable business users to embed real-life business constraints directly into optimization engines, ensuring that outcomes respect the constraints. Lastly, our platform empowers customers and partners to reach unprecedented levels of confidence in their decision-making processes.

3. Overview of Cai Optimization Package

The Cai optimization Package supports data science teams in finding optimal interventions in a structural causal model. It enables the maximization of user-defined objective functions, which can be any function that returns a scalar value. The package takes into account the initial data, data output by the structural causal model, and the current interventions, allowing for budget constraints to be set. This flexible framework can solve a variety of problems, such as optimizing business KPIs, finding the best actions within a certain budget, and designing better experiments.

4. Examples of Value-Driven Solutions

The Cai optimization package has helped numerous customers drive significant value and enable cross-collaboration with data science teams. One example of this is maximizing revenue given a change in budget. Another example is optimizing marketing allocation while retaining revenue. Additionally, the package has provided supply chain leaders with recommendations to optimize the allocation of available stock to fulfill sales orders while considering defined business rules.

5. Introduction to Algorithmic Recourse

Algorithmic recourse is another essential component of our decision optimization approach. It helps identify the optimal interventions required to achieve key business objectives. By exploring various counterfactual scenarios, the package discovers the actions needed to reverse unfavorable outcomes. One of the most valuable use cases for algorithmic recourse is preventing customer churn while respecting Relevant business constraints.

6. Individual Recourse

Within the algorithmic recourse framework, data scientists can leverage individual recourse. This type of recourse returns a set of actions for each individual sample, aiming to reverse undesired outcomes. Data scientists can instantiate the recourse engine and pass individual samples to obtain the recommended actions.

7. Macro Recourse

Macro recourse, the other type of recourse within the algorithmic recourse framework, provides a list of actions to maximize the reversal of undesired outcomes for the largest possible number of individuals in the provided dataset. By leveraging configurations and instantiating the system, data scientists can obtain recommendations for the entire dataset or a subset of it.

8. How to Deploy the Decision Optimization Approach

The deployment process for both the Cai optimization package and algorithmic recourse begins with instantiating and training a structural causal model. This model should have either rung 2 or rung 3 estimators, capable of predicting various levels of Pearl's ladder of causation. Once the model is ready, you can define the optimization objective, which can be any scalar function considering modified data from the model, intervention cost, and the budget. Using this approach, you can focus on real-life examples such as maximizing revenue based on budget changes.

9. Benefits of Decision Optimization

The decision optimization approach and the accompanying packages offer several benefits to enterprises. Firstly, decision-makers can leverage the outputs from decision optimization to optimize and transform their decision-making processes. Secondly, this approach ensures strong cross-collaboration between data science teams and business users, leading to informed and data-driven decisions that maximize business value.

10. Conclusion

In conclusion, decision optimization packages provide a unique opportunity to enhance decision-making processes by leveraging data science techniques and collaboration between teams. The Cai optimization package and algorithmic recourse offer value-driven solutions through accurate recommendations and interventions. By deploying this approach, businesses can drive optimal outcomes, maximize revenue, and achieve desired objectives while considering business constraints and rules.

Highlights

Our unique approach enables cross-collaboration between data science teams and decision makers.
Decision optimization packages generate accurate and actionable recommendations for solving business critical problems.
The Cai optimization package supports data science teams in finding optimal interventions in structural causal models.
Algorithmic recourse helps identify optimal interventions for achieving desired business objectives.
Individual and macro recourse provide actionable recommendations for reversing undesired outcomes.
The decision optimization approach empowers enterprises to optimize and transform their decision-making processes.
Strong collaboration between data science teams and business users is essential for successful decision optimization.
Deploying the decision optimization approach maximizes revenue, optimizes marketing allocation, and improves supply chain processes.

FAQ

Q: How can decision optimization packages benefit businesses? A: Decision optimization packages generate accurate recommendations, optimize decision-making processes, and enable cross-collaboration between data science teams and business users.

Q: What problems can the Cai optimization package solve? A: The Cai optimization package can solve problems such as optimizing business KPIs, finding the best actions within a budget, and designing better experiments.

Q: How does algorithmic recourse help prevent customer churn? A: Algorithmic recourse identifies optimal interventions to reverse unfavorable outcomes, including preventing customer churn while respecting business constraints.

Q: What is the difference between individual and macro recourse? A: Individual recourse provides specific actions for each individual sample, while macro recourse provides a list of actions for the entire dataset or a subset of it.

Q: How can businesses deploy the decision optimization approach? A: Businesses can deploy the decision optimization approach by instantiating and training a structural causal model, defining the optimization objective, and leveraging the Cai optimization package and algorithmic recourse modules.

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