# Posts by Tags

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Bayesian Statistics - Techniques and Models flashcards

less than 1 minute read

Published:

It’s again a statistics deck.

## Bayesian Statistics - From Concept to Data Analysis flashcards

less than 1 minute read

Published:

This blog discusses causal inference. What is this post about Bayesian Statistics then?

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #8: Inferred Causation, $IC$, and ${IC}^*$

Published:

We will talk about IC, $IC$, and ${IC}^*$ in this post. You get the difference.

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

## Pearls of Causality #6: Markov Conditions

Published:

The model zoo of Markovian conditions is fascinating confusing. Let there be light!

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Pearls of Causality: The Causal Dictionary

Published:

No one told me that I need a dictionary for learning causal inference. Indeed, there was none before. Now there is.

## Pearls of Causality #3: The properties of d-separation

Published:

A top-secret guide to d-separation. We will go deep, ready?

## Pearls of Causality #2: Markov Factorization, Compatibility, and Equivalence

Published:

This post deliberately (wink) tries to confuse you about the grand scheme of DAG equivalence. What a good deal, isn’t it?

## Pearls of Causality #1: DAGs, d-separation, conditional independence

Published:

d-separation is the bread and butter for deciding about conditional independence in DAGs. What is a DAG, anyway?

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Pearls of Causality #8: Inferred Causation, $IC$, and ${IC}^*$

Published:

We will talk about IC, $IC$, and ${IC}^*$ in this post. You get the difference.

## Rotating Features For Object Discovery

Published:

Structure is a useful but underleveraged inductive bias for representation learning.

## LaTeX tricks

Published:

Improve typesetting and save space in your submissions, who does not want that?

## Pearls of Causality #6: Markov Conditions

Published:

The model zoo of Markovian conditions is fascinating confusing. Let there be light!

## Pearls of Causality: The Causal Dictionary

Published:

No one told me that I need a dictionary for learning causal inference. Indeed, there was none before. Now there is.

## Pearls of Causality #11: Front- and Back-Door Adjustment

Published:

Two ways to shut the door before confounding enters the scene.

## Pearls of Causality #10: Interventions and Identifiability

Published:

Interventions in disguise.

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #8: Inferred Causation, $IC$, and ${IC}^*$

Published:

We will talk about IC, $IC$, and ${IC}^*$ in this post. You get the difference.

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

## Pearls of Causality #6: Markov Conditions

Published:

The model zoo of Markovian conditions is fascinating confusing. Let there be light!

## Pearls of Causality #5: Statistical vs Causal Inference

Published:

What you won’t be able to find in this post are unconditional claims of superiority of causal inference.

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Pearls of Causality #3: The properties of d-separation

Published:

A top-secret guide to d-separation. We will go deep, ready?

## Pearls of Causality #2: Markov Factorization, Compatibility, and Equivalence

Published:

This post deliberately (wink) tries to confuse you about the grand scheme of DAG equivalence. What a good deal, isn’t it?

## Pearls of Causality #1: DAGs, d-separation, conditional independence

Published:

d-separation is the bread and butter for deciding about conditional independence in DAGs. What is a DAG, anyway?

## A post series on Pearl: Causality

less than 1 minute read

Published:

A causality blog cannot exist without discussing Judea Pearl’s Causality book. Thus, I am paying my debt.

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Where is the nature of the relationship expressed in causal models?

Published:

Graphs don’t tell about the nature of dependence, only about its (non-)existence.

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Mathematical foundations for Geometric Deep Learning

Published:

Yes, abstract algebra is actually useful for machine learning.

## A post series on Pearl: Causality

less than 1 minute read

Published:

A causality blog cannot exist without discussing Judea Pearl’s Causality book. Thus, I am paying my debt.

## Welcome to my journey!

less than 1 minute read

Published:

A PhD student’s casual journey with causal inference.

## Where is the nature of the relationship expressed in causal models?

Published:

Graphs don’t tell about the nature of dependence, only about its (non-)existence.

## Pearls of Causality #11: Front- and Back-Door Adjustment

Published:

Two ways to shut the door before confounding enters the scene.

## Pearls of Causality #10: Interventions and Identifiability

Published:

Interventions in disguise.

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #8: Inferred Causation, $IC$, and ${IC}^*$

Published:

We will talk about IC, $IC$, and ${IC}^*$ in this post. You get the difference.

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

## Pearls of Causality #6: Markov Conditions

Published:

The model zoo of Markovian conditions is fascinating confusing. Let there be light!

## Pearls of Causality #5: Statistical vs Causal Inference

Published:

What you won’t be able to find in this post are unconditional claims of superiority of causal inference.

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Pearls of Causality: The Causal Dictionary

Published:

No one told me that I need a dictionary for learning causal inference. Indeed, there was none before. Now there is.

## Probabilistic Graphical Models 3 flashcards

less than 1 minute read

Published:

Not just parameter learning, but learning about parameter learning got easier today.

## Pearls of Causality #3: The properties of d-separation

Published:

A top-secret guide to d-separation. We will go deep, ready?

## Pearls of Causality #2: Markov Factorization, Compatibility, and Equivalence

Published:

This post deliberately (wink) tries to confuse you about the grand scheme of DAG equivalence. What a good deal, isn’t it?

## Probabilistic Graphical Models 2 flashcards

less than 1 minute read

Published:

If your goal is to be able to recall Sum-Product Belief Propagation even at 3a.m., this is the post you are looking for.

## Pearls of Causality #1: DAGs, d-separation, conditional independence

Published:

d-separation is the bread and butter for deciding about conditional independence in DAGs. What is a DAG, anyway?

## A post series on Pearl: Causality

less than 1 minute read

Published:

A causality blog cannot exist without discussing Judea Pearl’s Causality book. Thus, I am paying my debt.

## Probabilistic Graphical Models 1 flashcards

less than 1 minute read

Published:

To make learning probabilistic graphical models frictionless and more fun.

## Causality resources

Published:

Good resources matter, a lot.

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #2: Markov Factorization, Compatibility, and Equivalence

Published:

This post deliberately (wink) tries to confuse you about the grand scheme of DAG equivalence. What a good deal, isn’t it?

## Pearls of Causality #1: DAGs, d-separation, conditional independence

Published:

d-separation is the bread and butter for deciding about conditional independence in DAGs. What is a DAG, anyway?

## Pearls of Causality #11: Front- and Back-Door Adjustment

Published:

Two ways to shut the door before confounding enters the scene.

## Pearls of Causality #3: The properties of d-separation

Published:

A top-secret guide to d-separation. We will go deep, ready?

## Pearls of Causality #1: DAGs, d-separation, conditional independence

Published:

d-separation is the bread and butter for deciding about conditional independence in DAGs. What is a DAG, anyway?

## Higgins et al. - Towards a Definition of Disentangled Representations

Published:

Disentanglement is a concept rooted in geometric deep learning.

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

## Higgins et al. - Towards a Definition of Disentangled Representations

Published:

Disentanglement is a concept rooted in geometric deep learning.

## AMMI 3 Notes: Geometric priors I

Published:

In the previous post, we dived deep into abstract algebra to motivate why Geometric Deep Learning is an interesting topic. Now we begin the journey to show that it is also useful in practice. In summary, we know that symmetries constrain our hypothesis class, making learning simpler—indeed, they can make learning a tractable problem. How does this happen?

## Mathematical foundations for Geometric Deep Learning

Published:

Yes, abstract algebra is actually useful for machine learning.

## Higgins et al. - Towards a Definition of Disentangled Representations

Published:

Disentanglement is a concept rooted in geometric deep learning.

## AMMI 3 Notes: Geometric priors I

Published:

In the previous post, we dived deep into abstract algebra to motivate why Geometric Deep Learning is an interesting topic. Now we begin the journey to show that it is also useful in practice. In summary, we know that symmetries constrain our hypothesis class, making learning simpler—indeed, they can make learning a tractable problem. How does this happen?

## Mathematical foundations for Geometric Deep Learning

Published:

Yes, abstract algebra is actually useful for machine learning.

## Pearls of Causality #11: Front- and Back-Door Adjustment

Published:

Two ways to shut the door before confounding enters the scene.

## Pearls of Causality #10: Interventions and Identifiability

Published:

Interventions in disguise.

## Pearls of Causality #11: Front- and Back-Door Adjustment

Published:

Two ways to shut the door before confounding enters the scene.

## Pearls of Causality #10: Interventions and Identifiability

Published:

Interventions in disguise.

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #8: Inferred Causation, $IC$, and ${IC}^*$

Published:

We will talk about IC, $IC$, and ${IC}^*$ in this post. You get the difference.

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

Published:

## LaTeX tricks

Published:

Improve typesetting and save space in your submissions, who does not want that?

## Bayesian Statistics - Techniques and Models flashcards

less than 1 minute read

Published:

It’s again a statistics deck.

## Bayesian Statistics - From Concept to Data Analysis flashcards

less than 1 minute read

Published:

This blog discusses causal inference. What is this post about Bayesian Statistics then?

## Probabilistic Graphical Models 3 flashcards

less than 1 minute read

Published:

Not just parameter learning, but learning about parameter learning got easier today.

## Probabilistic Graphical Models 2 flashcards

less than 1 minute read

Published:

If your goal is to be able to recall Sum-Product Belief Propagation even at 3a.m., this is the post you are looking for.

## Probabilistic Graphical Models 1 flashcards

less than 1 minute read

Published:

To make learning probabilistic graphical models frictionless and more fun.

## Causality resources

Published:

Good resources matter, a lot.

## Rotating Features For Object Discovery

Published:

Structure is a useful but underleveraged inductive bias for representation learning.

## Probabilistic Graphical Models 3 flashcards

less than 1 minute read

Published:

Not just parameter learning, but learning about parameter learning got easier today.

## Probabilistic Graphical Models 2 flashcards

less than 1 minute read

Published:

If your goal is to be able to recall Sum-Product Belief Propagation even at 3a.m., this is the post you are looking for.

## Probabilistic Graphical Models 1 flashcards

less than 1 minute read

Published:

To make learning probabilistic graphical models frictionless and more fun.

## Causality resources

Published:

Good resources matter, a lot.

## AMMI 3 Notes: Geometric priors I

Published:

In the previous post, we dived deep into abstract algebra to motivate why Geometric Deep Learning is an interesting topic. Now we begin the journey to show that it is also useful in practice. In summary, we know that symmetries constrain our hypothesis class, making learning simpler—indeed, they can make learning a tractable problem. How does this happen?

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

## Bayesian Statistics - Techniques and Models flashcards

less than 1 minute read

Published:

It’s again a statistics deck.

## Pearls of Causality #5: Statistical vs Causal Inference

Published:

What you won’t be able to find in this post are unconditional claims of superiority of causal inference.

## Bayesian Statistics - From Concept to Data Analysis flashcards

less than 1 minute read

Published:

This blog discusses causal inference. What is this post about Bayesian Statistics then?

## Rotating Features For Object Discovery

Published:

Structure is a useful but underleveraged inductive bias for representation learning.