Causal Inference and Discovery in Python
Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
Delivery estimate
Please enter a valid 4-digit Australian postcode.
to ·
Delivery estimate
Please enter a valid 4-digit Australian postcode.
to ·
Causal Inference and Discovery in Python
Description
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more
- Discover modern causal inference techniques for average and heterogenous treatment effect estimation
- Explore and leverage traditional and modern causal discovery methods
Book Description
Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.
You'll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you'll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you'll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You'll further explore the mechanics of how "causes leave traces" and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.
By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.
What you will learn
- Master the fundamental concepts of causal inference
- Decipher the mysteries of structural causal models
- Unleash the power of the 4-step causal inference process in Python
- Explore advanced uplift modeling techniques
- Unlock the secrets of modern causal discovery using Python
- Use causal inference for social impact and community benefit
Who this book is for
This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.
Table of Contents
- Causality - Hey, We Have Machine Learning, So Why Even Bother?
- Judea Pearl and the Ladder of Causation
- Regression, Observations, and Interventions
- Graphical Models
- Forks, Chains, and Immoralities
- Nodes, Edges, and Statistical (In)dependence
- The Four-Step Process of Causal Inference
- Causal Models - Assumptions and Challenges
- Causal Inference and Machine Learning - from Matching to Meta- Learners
- Causal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and More
- Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond
- Can I Have a Causal Graph, Please?
(N.B. Please use the Read Sample option to see further chapters)
Shipping & Processing
This item is in stock with our supplier and usually ships within 3–5 business days.
Processing times
- In stock: Items marked “In Stock” are on our shelves. We pick, pack and ship them within 1–2 business days.
- In stock with our supplier: Items not marked “In Stock” are held by one of our Australian or overseas suppliers. We order them in for you and they usually dispatch within 2–5 business days (3–5 for some suppliers). The estimate next to the price is for this item.
- Special orders: Some supplier items, such as specialist and imported items, titles awaiting reprint and print-on-demand titles, take longer to arrive. If your order includes one, we’ll update you as soon as we hear from our supplier.
- Supplier delays: Occasionally a supplier is delayed, or their stock information turns out to be inaccurate. We’ll keep your order open for up to 60 days. If an item hasn’t shipped or isn’t on its way to us by then, we’ll cancel that item and refund it.
How accurate is availability?
Availability is based on the stock levels our suppliers tell us they hold, and we update it regularly. If a supplier issue affects your order, we’ll let you know with an updated estimate.
Delivery
- As soon as your order ships you’ll get an email with tracking.
- Australia Post usually delivers within 2–10 business days of shipping (around 4 in metro areas and around 6 in regional areas).
- If your order has more than one item, we hold the in-stock items until everything is ready, so we can ship your whole order together in one parcel.
- Shipping cost: Free on orders over $100 within Australia, otherwise $9.95 flat rate per order however many items you buy. Free shipping applies to Australian addresses only; delivery to New Zealand is $14.95 per order.
- Check your address: If an order goes to an incorrect address you gave us, or isn’t collected and Australia Post returns it to us, you’ll need to pay the postage to send it again.
Cancellations
Need to change or cancel an order? Contact us as soon as possible at contact@bookloop.com.au or through our contact page.
- Within 30 minutes: You can change or cancel your order for a full refund within 30 minutes of placing it.
- After 30 minutes: Your order is already being processed, so it can no longer be changed or cancelled. If we can accept a late cancellation, we may offer store credit instead of a refund.
- Items in transit: Once an item is on its way, either from our supplier to us or from us to you, it can’t be cancelled. If you no longer want it once it arrives, you can return it as a change of mind, with return postage at your cost. See Returns.
- Separate orders can’t be combined once they’ve been placed, and adding or swapping items can change the delivery estimate.
- Supplier delays: Occasionally a supplier is delayed, or their stock information turns out to be inaccurate. We’ll keep your order open for up to 60 days. If an item hasn’t shipped or isn’t on its way to us by then, we’ll cancel that item and refund it.
Faulty or damaged items are always covered. See Returns.
Returns
Damaged or incorrect items (within 30 days)
If an item arrives damaged or isn’t what you ordered, contact us within 30 days of delivery. We may ask for a photo, and if we need the item back we’ll send a prepaid return label, so it costs you nothing. You’ll receive a replacement or refund, as guaranteed under the Australian Consumer Law.
Change of mind (within 30 days)
We offer a refund or store credit for change of mind within 30 days of purchase when:
- you have your proof of purchase;
- the item is unused and in its original condition and packaging;
- it isn’t a sale item, special order, custom item or gift voucher.
You pay the postage to return change-of-mind items. Change-of-mind returns aren’t available for print-on-demand titles or bulk orders with special pricing.
Returned to sender
If an order is sent to an incorrect address you gave us, or isn’t collected and Australia Post returns it to us, you’ll need to pay the postage to send it again.