Scenario-Based F# Interview Questions and Answers   Scenario 1: Designing an Immutable Domain Model Q1: You're building a financial transaction system. How would you model a transaction immutably in F#? Answer: type Transaction = {     Id: Guid     FromAccount: string     ToAccount: string     Amount: decimal     Timestamp: DateTime } Why it matters: F# favors immutability for safety and concurrency. An immutable domain model prevents accidental state mutation, especially in multi-threaded systems like financial applications.   Scenario 2: REST API for a Blog Using Giraffe Q2: You’re building a blog platform. How would you handle a GET /posts/{slug} route using F# and Giraffe? Answer (simplified): let getPostHandler slug =     fun (next: HttpFunc) (ctx: HttpContext) ->         task {      ...

  Top Questions & Answers on   Causal Inference in Data Science  DoWhy, EconML, and CausalNex Causal inference is a growing field in data science that goes beyond correlation to understand cause-and-effect relationships. If you've ever asked, "What would happen if I changed X?" , then causal inference is the toolkit you need. In this article, we address the most frequently asked questions about the top three Python libraries for causal inference : DoWhy , EconML , and CausalNex . Whether you're a data scientist, economist, or machine learning practitioner, this will help you explore which tool is best for your use case.   What Is Causal Inference in Data Science? Causal inference is the process of using data to determine whether a cause-and-effect relationship exists between variables. Unlike correlation or prediction, it aims to answer “What if” scenarios, such as: ·          What if we increase the pri...

  Data Science : Causal Inference Programming  Libraries: DoWhy, EconML, and CausalNex Causal inference is revolutionizing how data scientists uncover relationships beyond correlation, moving toward identifying cause-and-effect dynamics. In business, healthcare, economics, and more, understanding “why” something happens is far more powerful than simply knowing “what” happened. In this article, we explore the top three causal inference libraries in data science programming: DoWhy , EconML , and CausalNex . These Python-based libraries enable robust modeling, policy simulation, and decision-making rooted in causality.   What is Causal Inference in Data Science? Causal inference refers to the process of determining whether one variable causes a change in another. Unlike traditional machine learning, which often captures patterns and correlations, causal inference helps in answering "What if?" and "Why?" questions. Key techniques in causal inference inc...