Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Causal Inference for Human-Language Model Collaboration

Bohan Zhang, Yixin Wang, Paramveer S. Dhillon

TL;DR

This work addresses causal inference in human-LM collaboration where text-based edits are high-dimensional treatments that defy standard $ATE$ analysis. It introduces Incremental Stylistic Effect (ISE), a local, style-focused estimand with non-parametric identification, and develops CausalCollab to estimate ISE in dynamic interactions by combining CVAEs with G-estimation. Through three diverse datasets, the authors demonstrate that accounting for stylistic changes and latent treatment representations substantially reduces confounding and improves counterfactual prediction compared to baselines. The approach offers a practical, scalable path toward understanding and improving human-LM collaboration by focusing on robust, style-level interventions rather than individual word edits, with implications for designing better interaction protocols and editing strategies.

Abstract

In this paper, we examine the collaborative dynamics between humans and language models (LMs), where the interactions typically involve LMs proposing text segments and humans editing or responding to these proposals. Productive engagement with LMs in such scenarios necessitates that humans discern effective text-based interaction strategies, such as editing and response styles, from historical human-LM interactions. This objective is inherently causal, driven by the counterfactual `what-if' question: how would the outcome of collaboration change if humans employed a different text editing/refinement strategy? A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality. To address this concern, we introduce a new causal estimand -- Incremental Stylistic Effect (ISE) -- which characterizes the average impact of infinitesimally shifting a text towards a specific style, such as increasing formality. We establish the conditions for the non-parametric identification of ISE. Building on this, we develop CausalCollab, an algorithm designed to estimate the ISE of various interaction strategies in dynamic human-LM collaborations. Our empirical investigations across three distinct human-LM collaboration scenarios reveal that CausalCollab effectively reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines.

Causal Inference for Human-Language Model Collaboration

TL;DR

This work addresses causal inference in human-LM collaboration where text-based edits are high-dimensional treatments that defy standard analysis. It introduces Incremental Stylistic Effect (ISE), a local, style-focused estimand with non-parametric identification, and develops CausalCollab to estimate ISE in dynamic interactions by combining CVAEs with G-estimation. Through three diverse datasets, the authors demonstrate that accounting for stylistic changes and latent treatment representations substantially reduces confounding and improves counterfactual prediction compared to baselines. The approach offers a practical, scalable path toward understanding and improving human-LM collaboration by focusing on robust, style-level interventions rather than individual word edits, with implications for designing better interaction protocols and editing strategies.

Abstract

In this paper, we examine the collaborative dynamics between humans and language models (LMs), where the interactions typically involve LMs proposing text segments and humans editing or responding to these proposals. Productive engagement with LMs in such scenarios necessitates that humans discern effective text-based interaction strategies, such as editing and response styles, from historical human-LM interactions. This objective is inherently causal, driven by the counterfactual `what-if' question: how would the outcome of collaboration change if humans employed a different text editing/refinement strategy? A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality. To address this concern, we introduce a new causal estimand -- Incremental Stylistic Effect (ISE) -- which characterizes the average impact of infinitesimally shifting a text towards a specific style, such as increasing formality. We establish the conditions for the non-parametric identification of ISE. Building on this, we develop CausalCollab, an algorithm designed to estimate the ISE of various interaction strategies in dynamic human-LM collaborations. Our empirical investigations across three distinct human-LM collaboration scenarios reveal that CausalCollab effectively reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines.
Paper Structure (38 sections, 1 theorem, 9 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 38 sections, 1 theorem, 9 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work:
  3. Causal Inference for Human-LM Collaboration
  4. A causal perspective on Human-LM collaboration
  5. The challenge of textual treatments in causal human-LM collaboration
  6. Incremental Stylistic Effect (ISE): A causal estimand for textual treatments
  7. CausalCollab: An algorithm for dynamic human-LM Collaboration
  8. Empirical Studies
  9. Experiment setup and evaluation metrics
  10. Results
  11. Conclusion
  12. Proof of \ref{['thm:id-ise']}
  13. Details of CausalCollab
  14. Monte Carlo for G-estimation
  15. Dataset Details
  16. ...and 23 more sections

Key Result

Theorem 1

Under (1) the positivity condition for $\{f_t(\cdot, \cdot)\}_{t=1}^T$, and (2) the sequential exchangeability condition, the ISE of the style change sequence $f_{1:T}$ can be non-parametrically identified by plugging eq:id-style-change into eq:ISE,

Figures (4)

  • Figure 1: An interactive view of human-LM Collaborative Writing. The writer iteratively selected and rewrote suggestions from the LM to make the article have a better outcome.
  • Figure 2: The causal graph of human-LM collaboration ($T$=2).
  • Figure 3: Three examples of $A_2$ with different lengths. The color of words is decided by their cosine distance to the treatment $z_2$ learned by the CVAE. The darker the color, the closer a word is to $z_i$. Stopwords are black.
  • Figure 4: Performances of our methods under different $\alpha \sim$ split, levels of noise $\sigma$, and dimensions of $z_i$ on the coauthor dataset. As $\alpha$ increases, the confounding correlation weakens, and our adjustment maintains similar counterfactual and observational performances. The method is robust to varying noise levels $\sigma$, keeping both counterfactual and observational performance low. The choice of $z_i$ dimension has minimal impact on performance, indicating that predictive treatments can be effectively represented in a low-dimensional space.

Theorems & Definitions (1)

  • Theorem 1: Non-parametric identification of ISE