To enhance surface property analysis for irrigation mapping, we compute a suite of spectral indices capturing vegetation health, water presence, and soil conditions12. Common vegetation indices such as NDVI, GNDVI, and CIgreen quantify canopy vigor and chlorophyll content, while EVI, SAVI, and MSAVI account for atmospheric and soil background effects [44, 68, 28].

Bayesian networks and causal models provide frameworks for handling queries about external interventions and counterfactuals, enabling tasks that go beyond what probability distributions alone can address. While these formalisms are often informally described as capturing causal knowledge, there is a lack of a formal theory characterizing the type of knowledge required to predict the effects of external interventions. This work introduces the theoretical framework of causal systems to clarify Aristotle's distinction between knowledge that and knowledge why within artificial intelligence. By interpreting existing artificial intelligence technologies as causal systems, it investigates the corresponding types of knowledge. Furthermore, it argues that predicting the effects of external interventions is feasible only with knowledge why, providing a more precise understanding of the knowledge necessary for such tasks.

In a world that constantly changes, it is crucial to understand how those changes impact different systems, such as industrial manufacturing or critical infrastructure. Explaining critical changes, referred to as concept drift in the field of machine learning, is the first step towards enabling targeted interventions to avoid or correct model failures, as well as malfunctions and errors in the physical world. Therefore, in this work, we extend model-based drift explanations towards causal explanations, which increases the actionability of the provided explanations. We evaluate our explanation strategy on a number of use cases, demonstrating the practical usefulness of our framework, which isolates the causally relevant features impacted by concept drift and, thus, allows for targeted intervention.

Pearl observes that causal knowledge enables predicting the effects of interventions, such as actions, whereas descriptive knowledge only permits drawing conclusions from observation. This paper extends Pearl's approach to causality and interventions to the setting of stratified abductive logic programs. It shows how stable models of such programs can be given a causal interpretation by building on philosophical foundations and recent work by Bochman and Eelink et al. In particular, it provides a translation of abductive logic programs into causal systems, thereby clarifying the informal causal reading of logic program rules and supporting principled reasoning about external actions. The main result establishes that the stable model semantics for stratified programs conforms to key philosophical principles of causation, such as causal sufficiency, natural necessity, and irrelevance of unobserved effects. This justifies the use of stratified abductive logic programs as a framework for causal modeling and for predicting the effects of interventions.

The original framework of Poole and Sato extended the logic programming language Prolog (Flach, 1994) with probabilistic facts. These are facts that are annotated with the probability that they are true; they play a role similar to the parentless nodes in Bayesian networks in that they are marginally independent of one another, and that the probabilistic dependencies are induced by the rules of the logic program. This resulted in the celebrated distribution semantics (Sato, 1995) that is the basis of probabilistic logic programming, and the corresponding learning algorithm in the PRISM language (Sato, 1995) constitutes - to the best of the authors' knowledge - the very first probabilistic programming language with built-in support for machine learning. The work of Sato and Poole has inspired many follow-up works on inference and learning, and has also introduced many variations and extensions of the probabilistic logic programming and its celebrated distribution semantics.

A ProbLog program is a logic program with facts that only hold with a specified probability. In this contribution we extend this ProbLog language by the ability to answer "What if" queries. Intuitively, a ProbLog program defines a distribution by solving a system of equations in terms of mutually independent predefined Boolean random variables. In the theory of causality, Judea Pearl proposes a counterfactual reasoning for such systems of equations. Based on Pearl's calculus, we provide a procedure for processing these counterfactual queries on ProbLog programs, together with a proof of correctness and a full implementation. Using the latter, we provide insights into the influence of different parameters on the scalability of inference. Finally, we also show that our approach is consistent with CP-logic, i.e. with the causal semantics for logic programs with annotated with disjunctions.

What do strange dogs pooping in your yard and the way some people are responding to Covid-19 have in common? Both are undesirable behaviors that can be hard to detect and correct – until now. Applying the phrase, "with conflict comes creativity," we got creative and designed and deployed an efficient and powerful ML/AI solution for detecting and correcting problems like these. One night I had walked down the hill in the dark to fetch our garbage cans. About 30 minutes later I began getting that familiar whiff.

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. A complementary Domino project is available. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. This may require a shift in mindset, particularly if moving from "traditional statistical analysis to causal analysis of multivariate data". As Domino is committed to providing the platform and tools data scientists need to accelerate their work, we reached out to Addison-Wesley Professional (AWP) Pearson for permission to excerpt "Causal Inference" from the book, Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. We appreciate the permissions to provide the chapter excerpt below as well as place the code within a complementary Domino project. We've introduced [in the book] a couple of machine-learning algorithms and suggested that they can be used to produce clear, interpretable results. You've seen that logistic regression coefficients can be used to say how much more likely an outcome will occur in conjunction with a feature (for binary features) or how much more likely an outcome is to occur per unit increase in a variable (for real-valued features). We'd like to make stronger statements. We'd like to say "If you increase a variable by a unit, then it will have the effect of making an outcome more likely." These two interpretations of a regression coefficient are so similar on the surface that you may have to read them a few times to take away the meaning. The key is that in the first case, we're describing what usually happens in a system that we observe. In the second case, we're saying what will happen if we intervene in that system and disrupt it from its normal operation. After we go through an example, we'll build up the mathematical and conceptual machinery to describe interventions. We'll cover how to go from a Bayesian network describing observational data to one that describes the effects of an intervention. We'll go through some classic approaches to estimating the effects of interventions, and finally we'll explain how to use machine-learning estimators to estimate the effects of interventions.

Technology giant Nvidia has revealed how one of its engineers has used machine learning at his home to keep cats off his prized lawn. Robert Bond, who has previously used the firm's Jetson TX1 platform to build a laser to take out the ants that appeared on his kitchen floor, used the same machine learning technology to turn his sprinklers into a smart identification system that could spot cats that appeared on or near the lawn before triggering the sprinklers to shoo the feline visitors away. "My wife is a gardener and she likes her garden to be tidy and clean," Robert said. The new Jetson TX1 is really good at running these neural nets." His system, as he explained in an Nvidia blog post, works by detecting motion.

We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.