Causal Inference is a set of tools used to scientifically prove cause and effect, very commonly used in economics and medicine. This series will go over the basics that any data scientist should understand about causal inference - and point them to the tools they would need to perform it.
From playlist Causal Inference - The Science of Cause and Effect
Statistical Inference for Causal Inference - Causal Inference
In this video I explain the concept of statistical inference for causal inference through a realistic group ideal experiment example. Enjoy! Here's the link to my previous Statistical Inference Introduction video if you haven't watched it yet: https://youtu.be/fEGc8ZqveXM
From playlist Causal Inference - The Science of Cause and Effect
We describe my favorite causal inference technique: the parametric G formula, my go-to for any standard observational causal inference problems
From playlist Causal Inference - The Science of Cause and Effect
Fundamental Question - Causal Inference
In this video, I define the fundamental question and problem of causal inference and use an example to further explain the concept.
From playlist Causal Inference - The Science of Cause and Effect
Ideal Experiment - Causal Inference
In this video, I give you more details about the fundamental question and the fundamental problem of causal inference with the help of an example (our ideal experiment).
From playlist Causal Inference - The Science of Cause and Effect
Sushmita Roy: "Regulatory network inference on developmental and evolutionary lineages"
Computational Genomics Winter Institute 2018 "Regulatory network inference on developmental and evolutionary lineages" Sushmita Roy, University of Wisconsin Madison Institute for Pure and Applied Mathematics, UCLA March 2, 2018 For more information: http://computationalgenomics.bioinfor
From playlist Computational Genomics Winter Institute 2018
Maria Rodriguez-Martinez: Network approaches for personalized medicine
In this talk, I will present current activities of the Computational Systems Biology group at IBM Research, Zurich, focused on the inference and exploitation of networks of molecular interactions. Focusing first on the problem of network inference, a long-standing challenge for which many
From playlist Analysis and its Applications
From causal inference to autoencoders, memorization & gene regulation - Caroline Uhler, MIT
Recent progress in genomics makes it possible to perform perturbation experiments at a very large scale. This motivates the development of a causal inference framework that is based on observational and interventional data. We characterize the causal relationships that are identifiable and
From playlist Statistics and computation
Genevera Allen, Rice University - Stanford Big Data 2015
Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.
From playlist Big Data in Biomedicine Conference 2015
Julio Saez-Rodriguez: Dynamic logic models complement machine learning for personalized medicine
In the second talk, I will present some of our work on this area. Our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and tra
From playlist Mathematics in Science & Technology
Gene Regulation in Space and Time by Caroline Uhler
Information processing in biological systems URL: https://www.icts.res.in/discussion_meeting/ipbs2016/ DATES: Monday 04 Jan, 2016 - Thursday 07 Jan, 2016 VENUE: ICTS campus, Bangalore From the level of networks of genes and proteins to the embryonic and neural levels, information at var
From playlist Information processing in biological systems
Jennifer Tour Chayes (Microsoft Research New England and Microsoft Research New York City) URL: https://www.icts.res.in/lecture/4/details/1644/ Description: Everywhere we turn these days, we find that networks can be used to describe relevant interactions.In the high tech world, we see th
From playlist Distinguished Lectures
Active Dendrites avoid catastrophic forgetting - Interview with the Authors
#multitasklearning #biology #neuralnetworks This is an interview with the paper's authors: Abhiram Iyer, Karan Grewal, and Akash Velu! Paper Review Video: https://youtu.be/O_dJ31T01i8 Check out Zak's course on Graph Neural Networks (discount with this link): https://www.graphneuralnets.c
From playlist General Machine Learning
Group Ideal Experiment - Causal Inference
In this video, I explain the concept of a group ideal experiment wherein I introduce some more causal inference terminology! I also go over the fundamental problem of causal inference and the problem of statistical inference. Enjoy!
From playlist Causal Inference - The Science of Cause and Effect
DSI | Data-Driven Mechanistic Models – Design Inference by Babak Shahbaba
Mechanistic models provide a flexible framework for modeling heterogeneous and dynamic systems in ways that enable prediction and control. In this talk, we focus on the application of mechanistic models for investigating dynamic biological systems. We show that by embedding these models in
From playlist DSI Virtual Seminar Series
Dan Geschwind: "Integrative Genomics in Neuropsychiatric Disorders"
Computational Genomics Summer Institute 2016 "Integrative Genomics in Neuropsychiatric Disorders" Daniel H. Geschwind MD PhD, UCLA Institute for Pure and Applied Mathematics, UCLA July 29, 2016 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2016
Tomislav Stankovski - Neural Cross-frequency Coupling: delta-alpha, resting state, anesthesia, sleep
Recorded 02 September 2022. Tomislav Stankovski of the Cyril and Methodius University of Skopje presents "Neural Cross-frequency Coupling Functions: delta-alpha coupling in resting state, anesthesia and sleep" at IPAM's Reconstructing Network Dynamics from Data: Applications to Neuroscienc
From playlist 2022 Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs (Paper Explained)
#ai #biology #neuroscience Backpropagation is the workhorse of modern deep learning and a core component of most frameworks, but it has long been known that it is not biologically plausible, driving a divide between neuroscience and machine learning. This paper shows that Predictive Codin
From playlist Papers Explained
Optimality principles and identification of dynamic models of biosystems
From playlist Spring 2018
Brief Introduction to Statistical Inference - Causal Inference
In this video, I briefly introduce the topic of Statistical Inference and go over its most fundamental concepts - those that we will use in this series. If you want to learn more about this stuff, check out this link to my entire series on Statistical Inference: https://www.youtube.com/pla
From playlist Causal Inference - The Science of Cause and Effect