Opening — Why this matters now Explainability for large language models has reached an uncomfortable stage of maturity. We have methods. We have surveys. We even have regulatory pressure. What we do not have—at least until now—is a reliable way to tell whether an explanation actually reflects how a model behaves, rather than how comforting it sounds. ...
Opening — Why this matters now Large Language Model (LLM) agents have crossed an uncomfortable threshold. They are no longer just autocomplete engines or polite chat companions; they are being entrusted with financial decisions, scientific hypothesis generation, and multi-step autonomous actions. With that elevation comes a familiar demand: explain yourself. Chain-of-Thought (CoT) reasoning was supposed to be the answer. Let the model “think out loud,” and transparency follows—or so the story goes. The paper behind Project Ariadne argues, with unsettling rigor, that this story is largely fiction. Much of what we see as reasoning is closer to stagecraft: convincing, articulate, and causally irrelevant. ...
Opening — Why this matters now Energy forecasting is no longer a polite academic exercise. Grid operators are balancing volatile renewables, industrial consumers are optimizing costs under razor‑thin margins, and regulators are quietly realizing that accuracy without robustness is a liability. Yet most energy demand models still do what machine learning does best—and worst: optimize correlations and hope tomorrow looks like yesterday. This paper argues that hope is not a strategy. ...
Generative AI still ships answers without warranties. Edgar Dobriban’s new review, “Statistical Methods in Generative AI,” argues that classical statistics is the fastest route to reliability—especially under black‑box access. It maps four leverage points: (1) changing model behavior with guarantees, (2) quantifying uncertainty, (3) evaluating models under small data and leakage risk, and (4) intervening and experimenting to probe mechanisms. The executive takeaway If you manage LLM products, your reliability roadmap isn’t just RLHF and prompt magic—it’s quantiles, confidence intervals, calibration curves, and causal interventions. Wrap these around any model (open or closed) to control refusal rates, surface uncertainty that matters, and measure performance credibly when eval budgets are tight. ...
When Hidden Variables Become Hidden Costs In causal inference, confounders are the uninvited guests at your data party — variables that influence both treatment and outcome, quietly skewing results. In healthcare, failing to adjust for them can turn life-saving insights into misleading noise. Traditionally, finding these culprits has been the realm of domain experts, a slow and costly process that doesn’t scale well. The paper from National Sun Yat-Sen University proposes a radical alternative: put Large Language Model (LLM)-based agents into the causal inference loop. These agents don’t just crunch numbers — they reason, retrieve domain knowledge, and iteratively refine estimates, effectively acting as tireless, always-available junior experts. ...
In asset management, few debates are more charged than the tug-of-war between causal purity and predictive utility. For years, a growing number of voices in empirical finance have argued that causal factor models are a necessary condition for portfolio efficiency. If a model omits a confounder, the logic goes, directional failure and Sharpe ratio collapse are inevitable. But what if this is more myth than mathematical law? A recent paper titled “The Myth of Causal Necessity” by Alejandro Rodriguez Dominguez delivers a sharp counterpunch to this orthodoxy. Through formal derivations and simulation-based counterexamples, it exposes the fragility of the causal necessity argument and makes the case that predictive models can remain both viable and efficient even when structurally misspecified. ...
When electricity markets were deregulated across many U.S. states in the 1990s, economists and policymakers hoped competition would lower consumer prices. But for decades, the results remained ambiguous—until now. A new paper, Causality analysis of electricity market liberalization on electricity price using novel Machine Learning methods, offers the most precise evaluation yet. Using cutting-edge causal machine learning models, the authors demonstrate that liberalization led to a 7% decrease in residential electricity prices in the short term—a finding with major implications for regulatory policy and infrastructure reform. ...
In the noisy world of algorithmic trading, volatility is often treated as something to manage or hedge against. But what if it could be a signal generator? Ivan Letteri’s recent paper proposes a novel trading framework that does just that: it treats mid-range volatility not as a nuisance, but as the key to unlocking directional causality between assets. From Volatility to Causality: The 4-Step Pipeline This is not your standard volatility arbitrage. The author introduces a four-stage pipeline that transforms volatility clusters into trading signals: ...
“In the absence of perfect knowledge, how do we still reason causally?” This paper tackles a profound and practical dilemma in causal inference: what if we don’t know the full causal graph? In real-world settings — whether in healthcare, finance, or digital platforms — complete causal diagrams are rare. Practitioners instead rely on causal abstractions: simplified, coarse-grained representations that preserve partial causal knowledge. But this raises a fundamental question: Which causal queries can still be identified under such abstraction? ...