We develop a criterion to certify whether causal effects are identifiable in linear structural equation models with latent variables. Linear structural equation models correspond to directed graphs whose nodes represent the random variables of interest and whose edges are weighted with linear coefficients that correspond to direct causal effects. In contrast to previous identification methods, we do not restrict ourselves to settings where the latent variables constitute independent latent factors (i.e., to source nodes in the graphical representation of the model). Our novel latent-subgraph criterion is a purely graphical condition that is sufficient for identifiability of causal effects by rational formulas in the covariance matrix. To check the latent-subgraph criterion, we provide a sound and complete algorithm that operates by solving an integer linear program. While it targets effects involving observed variables, our new criterion is also useful for identifying effects between latent variables, as it allows one to transform the given model into a simpler measurement model for which other existing tools become applicable.

There exist several approaches for estimating causal effects in time series when latent confounding is present. Many of these approaches rely on additional auxiliary observed variables or time series such as instruments, negative controls or time series that satisfy the front- or backdoor criterion in certain graphs. In this paper, we present a novel approach for estimating direct (and via Wright's path rule total) causal effects in a time series setup which does not rely on additional auxiliary observed variables or time series. This approach assumes that the underlying time series is a Structural Vector Autoregressive (SVAR) process and estimates direct causal effects by solving certain linear equation systems made up of different covariances and model parameters. We state sufficient graphical criteria in terms of the so-called full time graph under which these linear equations systems are uniquely solvable and under which their solutions contain the to-be-identified direct causal effects as components. We also state sufficient lag-based criteria under which the previously mentioned graphical conditions are satisfied and, thus, under which direct causal effects are identifiable. Several numerical experiments underline the correctness and applicability of our results.

Many quadrupedal robots can adeptly handle uneven or sloped terrain, but only if the ground beneath them is relatively stable. Factor in slippery or muddy surroundings and four-legged machines may quickly falter or fail completely. But one engineering team believes they found a solution in mimicking animals often found in boggy habitats. According to a study published in Bioinspiration & Biomimetics by researchers at Estonia's Tallinn University of Technology (TalTech), dog bots could soon take their cues from giant moose. "[M]ost robots cannot access a wide range of highly important terrestrial environments, including wetlands, bogs, coastal marshes, river estuaries and fields, which are abundant in nature," explained TalTech biorobotics professor and team lead, Maarja Kruusmaa, in an accompanying statement on January 2nd. Ungulates (split-hooved animals like cattle and moose), however, are evolutionarily equipped to handle these often sticky situations.

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach.

In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges. The orientation schemes assess algebraic relations among moments of the data-generating distribution and are computationally inexpensive. We establish high-dimensional consistency results for our approach and compare different algorithmic versions in numerical experiments.

We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i.e., when directed acyclic graphs (DAGs) have the same set of unconditional d-separation statements. Each unconditional equivalence class (UEC) is uniquely represented with an undirected graph whose clique structure encodes the members of the class. Via this structure, we provide a transformational characterization of unconditional equivalence; i.e., we show that two DAGs are in the same UEC if and only if one can be transformed into the other via a finite sequence of specified moves. We also extend this characterization to the essential graphs representing the Markov equivalence classes (MECs) in the UEC. UECs partition the space of MECs and are easily estimable from marginal independence tests. Thus, a characterization of unconditional equivalence has applications in methods that involve searching the space of MECs of Bayesian networks. Keywords: unconditional equivalence; marginal independence structure; undirected graphs; directed acyclic graphs; chain graphs; Markov equivalence.

Tragedy befalls a young samurai, Hiroki, and his village; in the aftermath, his duty toward his love and community are tested. The storytelling is saved partially by well-written and acted dialogue, all in Japanese. While the premise and story beats are predictable, the panache and drama of each line reading keeps it interesting. The team used a Japanese consultant to make sure the dialogue was historically accurate. It also helps to have an easy-to-hate villain in the demonic Kagerou, who cuts an imposing figure, looming over our hero's life.

Hollywood's Captain Kirk, 90-year-old William Shatner, blasted into space Wednesday in a convergence of science fiction and science reality, reaching the final frontier on board a ship built by Jeff Bezos's Blue Origin company. The Star Trek hero became the oldest person to ride a rocket, eclipsing the previous record -- set by a passenger on a similar jaunt on a Bezos spaceship in July -- by eight years. Dressed in a royal blue flight suit, Shatner joined three fellow passengers, four to five decades younger, on board the fully automated capsule that took off from remote West Texas for an up-and-down flight scheduled to last just 10 minutes or so. The spaceship aimed for an altitude of 106 kilometres (66 miles), at the fringes of space, after which the capsule was set to parachute back to the desert floor. Sci-fi fans revelled in the opportunity to see the man best known as the stalwart Captain James T Kirk of the starship Enterprise boldly go where no star of American TV has gone before.

If you have finally decided to take the path from Excel-copy-and-paste to reproducible data science, then you will need to know the best route to take. The good news is that there is an abundance of free resources to get you there and awesome online communities to help you along the way. The bad news is that it can get overwhelming to pick which resources to take advantage of. This here is a no-nonsense guide that you can follow without regret, so you can spend less time worrying about the trail and more time trekking it. It's based on the lessons I learned when I went from a renewable energy project engineer who had never taken a statistics class to the head of a major data platform. At the trailhead for this journey, you can find an army of educated individuals doing data analysis by necessity, not passion.

You are a Software Engineer. You notice Artificial Intelligence, Machine Learning, Deep Learning, Data Science buzzwords all around. You wonder what these phrases mean, whether all this is for real and useful or is yet another hype and passing fad. You want to figure out how it is changing or will change the computer/IT industry, and why you should care, if at all. You google about it, you read various articles, blogs, and tutorials.