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Michael Knaus

Michael Knaus✓ Add us as a preferred source on Google

Mastodon @mcknaus@econtwitter.net
⚔ Compare ☆ Follow Own this channel?
🔄 Data last refreshed 1 day ago
Followers
85
Account age
3 yrs
🎁 Free analysisfor Michael Knaus
🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

Interested in learning #causalML for policy evaluation/learning? I consolidated my teaching material from different courses for Master and PhD Economics students. Result: - 10 slide decks - 22 R notebooks See https://github.com/MCKnaus/causalML-teaching and thread below6 views Interested in learning #causalML for policy evaluation/learning? I consolidated my teaching material from different courses for Master and PhD Economics students. Result: - 10 slide decks - 22 R notebooks See https://github.com/MCKnaus/causalML-teaching and thread below

📊 Post engagement

6
Avg engagement / post
7.1%
Engagement vs followers
Nov 2022
On Mastodon since

🔥 Top post: Interested in learning #causalML for policy evaluation/learning? · 6 likes + reposts

📊 Activity & format

Posting cadence
1.8 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 0 text · 12 image · 0 video.
Follower / following
1.7×
Follows 51 back. A more reciprocal / networked account.

Recent posts

View on Mastodon ↗
🔥 Top post Interested in learning #causalML for policy evaluation/learning? I consolidated my teaching material from different courses for Master and PhD Economics students. Result: - 10 slide decks - 22 R notebooks See https://github.com/MCKnaus/causalML-teaching and thread below ★ 6
9. Offline/online policy learning - Toy example to motivate that policy learning is different from CATE prediction - Policy learning (binary and multiple treatments) - Pitch of multi-armed bandits https://nbviewer.org/github/MCKnaus 8. Heterogeneous effects with inference - Generic ML of with BLP/GATES/CLAN (low-dimensional summary statistics of predicted CATEs) - DR-learner with kernel or series regression https://nbviewer.org/github/MCKnaus 7. Predicting treatment effects aka CATE estimation - Why this is a nontrivial problem and T-learner not the end of the story - Causal tree and forest of Imbens & @susanathey et al. - R-learner (Nie & Wager) - DR-learner of Kennedy htt… 6. The generic Double ML recipe - Double ML with linear Neyman orthogonal scores - Average treatment effect on the treated - IV estimation with constant effects and for LATE - Influence functions and their usage https://nbviewer.org/githu… 5. Estimation of average PO and ATE allowing for heterogeneous effects - Double ML for augmented inverse probability weighting (AIPW) - Definition of AIPW ATE pseudo-outcome that has a prominent role in following lectures https://nbviewer.… 4. Constant effect estimation - Double Selection - Double ML for partially linear regression - Introduction of Neyman orthogonality https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML4_DS_PLR.pdf 3. Causal inference intro/recap: - Potential outcomes - Experiments - Pitch of structural causal models and directed acyclic graphs - Pitch of single-world intervention graphs https://nbviewer.org/github/MCKnaus 2. Supervised ML intro/recap through the causal ML lense: - Lasso and especially Post-Lasso as basis of Double Selection later - Regression trees and random forest as basis of Causal Tree and Forest later https://nbviewer.org/github/MCKnaus 1. Stats/’metrics recap: - Conditional Expectation Functions and how to model them - Convergence rates Both play a crucial role for #DoubleML and students tend to have no good intuition what the latter means https://nbviewer.org/github/MCK… 0. Welcome slides - Pep talk - Managing expectations https://nbviewer.org/github/MCKnaus and more

🐘 Community & instance

Home server
econtwitter.net
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
✅ Link-verified
Verified link
Proved ownership of a website linked on their profile — Mastodon's green-check verification, a real identity signal rather than a paid badge.
On Mastodon since
Nov 2022
Joined in the Twitter-exodus wave of late 2022 — part of the migration that made Mastodon a real destination.

💡 Facts

🗓️Joined Mastodon in 2022 — 3 years ago.
👁️Averages 6 views per post.
📤Posts about 1.8× per week.

🕵️ Fake follower check

Estimated
75/100
Good Credibility score
92%
Real Real audience
Low Fake-follower risk
High Data confidence
  • Est. 92% real, active audience · Low fake-follower risk.
  • Engagement (~7.1% of followers engage each post) is around typical for Mastodon.
  • Verified account.
  • Established account (3+ years old).

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

Assistant Professor of "Data Science in Economics" at Uni Tübingen. Interested in the intersection of causal inference and machine learning #causalML Hopes to share some useful insights in this realm.

📸 Gallery

9. Offline/online policy learning - Toy example to motivate that policy learning is different from CATE prediction - Policy learning (binary and multiple treatments) - Pitch of multi-armed bandits https://nbviewer.org/github/MCKnaus 8. Heterogeneous effects with inference - Generic ML of with BLP/GATES/CLAN (low-dimensional summary statistics of predicted CATEs) - DR-learner with kernel or series regression https://nbviewer.org/github/MCKnaus 7. Predicting treatment effects aka CATE estimation - Why this is a nontrivial problem and T-learner not the end of the story - Causal tree and forest of Imbens & @susanathey et al. - R-learner (Nie & Wager) - DR-learner of Kennedy https://nbviewer.org/github/MCKnaus 6. The generic Double ML recipe - Double ML with linear Neyman orthogonal scores - Average treatment effect on the treated - IV estimation with constant effects and for LATE - Influence functions and their usage https://nbviewer.org/github/MCKnaus 5. Estimation of average PO and ATE allowing for heterogeneous effects - Double ML for augmented inverse probability weighting (AIPW) - Definition of AIPW ATE pseudo-outcome that has a prominent role in following lectures https://nbviewer.org/github/MCKnaus 4. Constant effect estimation - Double Selection - Double ML for partially linear regression - Introduction of Neyman orthogonality https://nbviewer.org/github/MCKnaus/causalML-teaching/blob/main/Slides/CML4_DS_PLR.pdf 3. Causal inference intro/recap: - Potential outcomes - Experiments - Pitch of structural causal models and directed acyclic graphs - Pitch of single-world intervention graphs https://nbviewer.org/github/MCKnaus 2. Supervised ML intro/recap through the causal ML lense: - Lasso and especially Post-Lasso as basis of Double Selection later - Regression trees and random forest as basis of Causal Tree and Forest later https://nbviewer.org/github/MCKnaus

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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