NEC Corporation (NEC; TSE: 6701) today announced hootfolio, Inc., a provider of causal analysis AI solutions that support decision making by visualizing the cause-and-effect relationships of things from data, launched business on January 1, 2025.

hootfolio, Inc., a Provider of Causal Analysis Technology, Launches Business Development: Press Releases | NEC

Causal

Learn how counterfactual forecasting helps data scientists measure true revenue impact by simulating causal scenarios beyond traditional time series models. 

From Time Series to Causal Scenarios: A Statistical Guide to Counterfactual Forecasting

Learn how to measure marketing impact without A/B tests using causal inference, Diff-in-Diff, synthetic control, and GeoLift.

When A/B Tests Aren’t Possible, Causal Inference Can Still Measure Marketing Impact

The Sun as the First Cause. Toward a Radical Reassessment of the Ontology of Life, Evolution, and Consciousness

Solar Reality - A Radical Reassessment of Life, Intelligence, and Causality

ScrollForge introduces Causal Graph Programming, a new programming paradigm that unifies frontend, backend, and styling through share 

Rethinking Reactivity: ScrollForge Links UI and Style Through a Single Causal Graph

What if karma isn’t a moral law — but an engineering problem?

The Science of Causality and the Resetting of Karma: A Manifesto for a New Way of Thinking 

Analyzing how ApeCoin price impacts Otherside land value using Granger causality tests to uncover metaverse wealth effects during market downturns.

Granger Causality and the Negative Wealth Effect in BAYC-Linked Virtual Assets

EffConPy is an open-source Python library designed to study time series beyond correlation and prediction. It provides many tools for causal discovery.

EffConPy: Open Source Causality Discovery in Python

Using causal forest and difference-in-differences methods, this study reveals gender bias in 'The Voice' blind auditions. 

Using Causal Forests to Unveil Gender Bias in Hiring

Causal analysis background and overview of different techniques to perform a causal analysis.

Causal Analysis – Experimentation (AB Testing) and Statistical Techniques

Causal Impact analysis is a valuable tool, but it comes with its set of limitations that practitioners need to be mindful of.

Causal Impact Analysis as an Alternative to A/B Testing

We will guide you through a specific dataset, demonstrating how to implement the library and interpret results. 

Unveiling Causal Impact: From Theory to Practice

Connecting the Dots: Proving Causal Relationships in Cluster Experiments with Network Interference

Proofs and Insights into Causal Clustering Experiments under Network Interference 

Explore the endogenous peer effects associated with Causal Clustering. 

Causal Clusterings in the Presence of Endogenous Peer Effects 

A list of the references drawn from for the research conducted by Davide Viviano and team on Designing Cluster Experiments Under Network Interference

Causal Clustering: Design of Cluster Experiments Under Network Interference: References

Explore actionable insights from the research led by Davide Viviano and team on causal clustering. 

Navigating Network Selection and Design with Causal Clustering

Gain insights into optimizing experimental designs for global treatment effects while considering spillover effects in network experiments.

Causal Clustering: Assessing Spillover Effects in Experimental Designs

Explore an econometric framework for designing cluster experiments in network settings to estimate global treatment effects. 

Causal Clustering: Design of Cluster Experiments Under Network Interference

... in a world full of data, we can understand the impact with clever methods. Meet Granger causality.

Granger Causality: Principle of Cause and Effect Explained

Explainable AI improves microservices resilience by making AI decisions auditable, reducing MTTR, and building trust in finance systems.

Building Resilient Financial Systems With Explainable AI and Microservices

A 12B parameter transformer trained on 250M image-text pairs generates high-quality, zero-shot images and emerging image-to-image capabilities.

DALL·E Research Paper: High Quality Image Generation Without Using Any of the Training Labels

LLaMA offers 7B–65B parameter language models trained on public data, outperforming GPT-3 and enabling open, scalable AI research.

To Achieve State-of-the-Art Performance by Training Exclusively on Publicly Available Data

RAG optimizes language model outputs by having them reference external knowledge bases before generating responses. 

9 RAG Architectures Every AI Developer Should Know: A Complete Guide with Examples

Tesla has the data ocean. Waymo has the ground truth. Nvidia has the compute. Who wins the Gran Truismo of autonomous driving?

Gran Turismo 2026: The Great AI Showdown for Autonomous Driving

Before ethics becomes a policy, a guideline, or a line of code, it must first exist as a human capacity. 

The Workplace as an Ethical Laboratory. Social and Emotional Experience as the Ground of Ethics

Causal is the best business planning platform for finance and beyond. With Causal, you can build models effortlessly, connect them directly to your data,and share them with beautiful visuals

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