Nathan Kallus (@nathankallus) / X

Nathan Kallus

320 posts

Nathan Kallus

@nathankallus

🏳️‍🌈👨‍👨‍👧‍👦 Assoc Prof @Cornell @Cornell_Tech @Netflix @NetflixResearch causal inference, experimentation, optimization, RL, statML, econML, fairness

New York, NY

Joined December 2010

Nathan Kallus
@nathankallus
Nov 6, 2023
The Machine Learning & Inference Research team I co-lead @netflix @NetflixResearch is hiring PhD interns for Summer 2024. Looking for a research internship (tackling industry problems while also focusing publishable research!)? Apply thru this listing: jobs.netflix.com/jobs/300628646
60K
Nathan Kallus
@nathankallus
Nov 3, 2022
The Product ML Research team I co-lead @netflix @NetflixResearch is hiring! Want to do ML research that drives basic science+pubs *and* business impact? Are you a deep thinker *and* a builder? Join us! jobs.netflix.com/jobs/239968902 Still in PhD? Intern with us! jobs.netflix.com/jobs/234882269
Nathan Kallus
@nathankallus
Aug 6, 2022
I am boycotting @informs2022. We cannot ask our woman colleagues to travel to a state where their health is put at danger and their choices limited about their own bodies. I am calling on @INFORMS to move venues from Indiana or change to virtual-only in light of the new law.
Nathan Kallus
@nathankallus
Dec 28, 2019
My research group (located at Cornell Tech campus in NYC) is looking to recruit a postdoc to work on topics related to causal inference, fairness in ML, and sequential decision making (bandits+RL). Positions are renewable (1-2 years). Please retweet to spread the word. 🙏
Nathan Kallus
@nathankallus
Sep 4, 2021
Excited to be co-organizing the NeurIPS 2021 Workshop on Causal Inference Challenges in Sequential Decision Making happening Dec 14 online. Please consider submitting contributions. CfP on website. Due date 9/30.
sites.google.com
Causal Sequential Decisions
Important dates: Submission deadline: October 7th, 2021 (AOE) September 30th, 2021 (AoE) Submissions accepted via CMT Workshop: December 14th, 2021 Sequential decision-making problems appear in...
Nathan Kallus
@nathankallus
Feb 2, 2021
Offline #ReinforcementLearning converges faster than you think! Offline RL is about learning new dynamic decision policies from existing data -- crucial in high-stakes domains like medicine. Theory predicts its regret converges as 1/√n. But when we run a sim we see 1/n 🤔🧵
Nathan Kallus
@nathankallus
Dec 14, 2019
My favorite part is finally here: the panel discussion!! With our awesome lineup of speakers, moderated by @david_sontag. At the #NeurIPS2019 causal ML workshop “Do the Right Thing”
Nathan Kallus
@nathankallus
Dec 14, 2019
.@alexdamour tells us about deconfounding scores, which generalize propensity and prognostic scores and help with covariate reduction, at the #NeurIPS2019 causal ML workshop “Do the Right Thing”
Nathan Kallus
@nathankallus
Sep 25, 2020
Replying to @yisongyue
Missing options: c. Raging pandemic. d. Future of the country you live in.
Nathan Kallus
@nathankallus
Feb 12, 2020
Very excited to share new work with @angelamczhou on partial identification in off-policy evaluation (OPE) in infinite-horizon RL when there are unobserved confounders: arxiv.org/abs/2002.04518. OPE is crucial for RL applications where exploration is limited, like medicine.👩‍⚕️ 1/n
arxiv.org
Confounding-Robust Policy Evaluation in Infinite-Horizon...
Off-policy evaluation of sequential decision policies from observational data is necessary in applications of batch reinforcement learning such as education and healthcare. In such settings,...
Nathan Kallus
@nathankallus
Sep 20, 2019
Personalized interventions using heterogeneous causal effects are the next big thing. But are they fair? Impossible to say: standard disparity measures are unidentifiable! In arxiv.org/abs/1906.01552 @angelamczhou & I give ways to credibly assess fairness despite this #NeurIPS2019
Nathan Kallus
@nathankallus
Jan 13, 2020
We cannot fix what we cannot measure! Thank you @NSF for funding my FAI proposal on *credible* fairness assessments and robustly fair algorithms: nsf.gov/awardsearch/sh… Proud+excited to be working with the amazing people at nycja.org on this project.
Nathan Kallus
@nathankallus
Jul 14, 2020
Very excited to be involved in four papers being presented at @icmlconf #ICML2020 this week. A short thread spotlighting the papers with just *one* sentence each:
Nathan Kallus
@nathankallus
Dec 23, 2021
Excited to post new paper with the amazing @Jacobb_Douglas & Kevin Guo "Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding" arxiv.org/abs/2112.11449 In this time of uncertainty it's good to have some checks on your causal inferences 1/n