Lifeboat Foundation

This tutorial aims to provide a survey of the Bayesian perspective of causal inference under the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects, and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low and high dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples.

Instructor:

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All the courses can be completed within 1 hour.

Four mathematicians have estimated the chances that there’s a clear path through a random maze.

Actually, nothing is wrong with it if you are a computer science major. It’s just that it has no place in the philosophy department.

From the point of anyone wanting to work in natural language, symbolic logic has all of the vices of mathematics and none of its virtues. That is, it is obscure to the point of incomprehensibility (given the weak neurons of this English major at any rate), and it leads to no useful outcome in the domain of human affairs. This would not be so bad were it not for all those philosophy major curricula that ask freshmen to take a course in it as their “introduction” to philosophy. For anyone looking to explore the meaning of life, this is a complete turnoff.

What were the philosophy mavens thinking?

Quantinuum unveiled its H2-1 quantum computer with 56 trapped-ion qubits that further improves its market-leading fidelity.

What is the red line in the ability to imitate or simulate the commonly recognizable characteristics of a popular or non-popular person when we seek to assess the potential for unethical persuasion of technologies like GenAI?

What responsibility does the creator bear for the intrinsic consequences related to the unethical use of a person’s identity as a lever of persuasion through technology?

Persuasion has no dark side per se: only the intentions of those who wield it do, and GenAI is not inherently endowed with such intentions, neither for itself nor by itself.

Discover the groundbreaking Self-Interacting Dark Matter (SIDM) theory that suggests dark matter particles might collide and interact with each other. Learn how recent studies on the El Gordo galaxy cluster support this revolutionary idea, potentially changing our understanding of the universe’s structure and evolution. Dive into the cosmic dance and stay updated with the latest space discoveries!

Chapters:
00:00 Introduction.
00:44 The Dance of Self-Interacting Dark Matter.
02:39 Unveiling the Strengths and Weaknesses of CDM and SIDM
05:14 Exploring Dark Matter: Methods and Future Prospects.
09:20 Outro.
09:37 Enjoy.

Continue reading “New Theory Changes Everything: SIDM and Dark Matter Collision!” »

‘Thinking at a distance’ with large language models sparks human-AI cognitive capacity transcending biological limits, but it risks existential ‘entangled mind’ miscalibration.

When it comes to development, an epigenetic clock may be responsible for human neurons’ slower maturation.