We introduce DoWhy-GCM, an extension of the DoWhy Python library, that leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation questions, with DoWhy-GCM, users can ask a wide range of additional causal questions, such as identifying the root causes of outliers and distributional changes, causal structure learning, attributing causal influences, and diagnosis of causal structures. To this end, DoWhy-GCM users first model cause-effect relations between variables in a system under study through a graphical causal model, fit the causal mechanisms of variables next, and then ask the causal question. All these steps take only a few lines of code in DoWhy-GCM. The library is available at https://github.com/py-why/dowhy.

Learning models that offer robust out-of-distribution generalization and fast adaptation is a key challenge in modern machine learning. Modelling causal structure into neural networks holds the promise to accomplish robust zero and few-shot adaptation. Recent advances in differentiable causal discovery have proposed to factorize the data generating process into a set of modules, i.e. one module for the conditional distribution of every variable where only causal parents are used as predictors. Such a modular decomposition of knowledge enables adaptation to distributions shifts by only updating a subset of parameters. In this work, we systematically study the generalization and adaption performance of such modular neural causal models by comparing it to monolithic models and structured models where the set of predictors is not constrained to causal parents. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization. We also found that the effects are more significant for sparser graphs as compared to denser graphs.

The services that will work best for you will depend on your specific use case and your team's level of expertise. Because it takes a lot of effort and ML expertise to build and maintain high quality ML models, a general rule of thumb is to use pretrained models or AI solutions whenever possible -- that is, when they fit your use case. If your data is structured, and it's in BigQuery, and your users are already comfortable with SQL, then choose BigQuery ML. If you realize that your use case requires writing your own model code, then use custom training options in Vertex AI. Let's look at your options in some more detail.

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In this post we're going to discuss a commonly used machine learning model called decision tree. Decision trees are preferred for many applications, mainly due to their high explainability, but also due to the fact that they are relatively simple to set up and train, and the short time it takes to perform a prediction with a decision tree. Decision trees are natural to tabular data, and, in fact, they currently seem to outperform neural networks on that type of data (as opposed to images). Unlike neural networks, trees don't require input normalization, since their training is not based on gradient descent and they have very few parameters to optimize on. They can even train on data with missing values, but nowadays this practice is less recommended, and missing values are usually imputed.

Let's assume that we have a labeled dataset with 10 samples in total. What the Decision Trees do is simple: they find ways to split the data in a way such as that separate as much as possible the samples of the classes (increasing the class separability). In the above example, the perfect split would be a split at x 0.9 as this would lead to 5 red points being at the left side and the 5 blue at the right side (perfect class separability). Each time we split the space/data like that, we actually build a decision tree with a specific rule. Here we initially have the root node containing all the data and then, we split the data at x 0.9 leading to two branches leading to two leaf nodes.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This is going to be part 4 of the Explaining Machine Learning Algorithms To A 10-Year Old series.

The Decision Tree is a machine learning algorithm that takes its name from its tree-like structure and is used to represent multiple decision stages and the possible response paths. The decision tree provides good results for classification tasks or regression analyses. With the help of the tree structure, an attempt is made not only to visualize the various decision levels but also to put them in a certain order. For individual data points, predictions can be made, for example, a classification by arriving at the target value along with the observations in the branches. The decision trees are used for classifications or regressions depending on the target variable.

The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.

OpenSea is putting in place a new system to spot NFT fakes and verify accounts, in an effort to cut down on the industry's growing fraud problem. In a couple of blog posts, the NFT marketplace detailed what changes users can expect, including opening up verification to more users, automated and human-assisted removal of so-called "copymints" or fake copies of authentic NFTs and changes to how collection badges -- which identify NFT collections with high sales volume or interest -- are doled out on the marketplace. First off, OpenSea will use a two-part system to detect fakes that combine both image recognition tech and human reviewers. The company says its new system will continuously scan all NFT collections (including newly minted assets) to spot any potential fakes. "Our new copymint prevention system leverages computer-vision tech to scan all NFTs on OpenSea (including new mints). The system then matches these scans against a set of authentic collections, starting with some of the most copy-minted collections -- we'll look for flips, rotations & other permutations," wrote OpenSea's Anne Fauvre-Willis in the post.