Summary Automated Social Science Structural Causal Model Approach benjaminmanning.io
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The Automated Social Science Model Approach combines structural causal models and large language models to create and evaluate hypotheses.
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Key Points
- Automated Social Science: A Structural Causal Model-Based Approach using SCMs and LLMs for hypothesis generation and testing
- Results from simulations in scenarios like negotiation, bail hearing, job interview, and auction demonstrate the effectiveness of the approach
- Importance of identifying causal structure ex-ante and the limitations of determining causal relationships ex-post
- System autonomously generates hypotheses as SCMs by querying LLM for relevant agents, causes, and outcomes
- Comparison of results to predictions made by LLM and auction theory highlights advantages and limitations of SCM-based approach
- Use of SCMs provides unbiased measurements of downstream endogenous outcomes and allows for identification of coefficients
- Challenges in identifying causal relationships when the underlying structure is unknown and the significance of avoiding bad controls
- SCM-based approach offers automation of hypothesis generation and experimental testing, providing insights not immediately available through direct elicitation
Summaries
18 word summary
Automated Social Science Model Approach uses structural causal models and large language models to generate and test hypotheses.
59 word summary
The Automated Social Science Structural Causal Model Approach, developed by Benjamin S. Manning, Kehang Zhu, and John J. Horton, uses structural causal models and large language models to autonomously generate and test social scientific hypotheses. Results from four social scenarios showed significant causal effects, automating hypothesis generation and experimental testing while emphasizing interpretability and automation in social science research.
133 word summary
The Automated Social Science Structural Causal Model Approach, developed by Benjamin S. Manning of MIT and Kehang Zhu of Harvard, along with John J. Horton of MIT & NBER, uses structural causal models (SCM) and large language models (LLM) to autonomously generate and test social scientific hypotheses. The system queries an LLM to generate hypotheses as SCMs, constructs agents, and generates survey questions to gather data. Results from four social scenarios showed significant causal effects, such as in bargaining over a mug and a bail hearing. The SCM-based approach automates hypothesis generation and experimental testing, revealing information not immediately available through direct elicitation. It also helps avoid misidentification by assuming or searching for causal structure in data. The study emphasizes the importance of interpretability and automation in hypothesis generation for social science research.
408 word summary
The Automated Social Science Structural Causal Model Approach, developed by Benjamin S. Manning of MIT and Kehang Zhu of Harvard, along with John J. Horton of MIT & NBER, presents an innovative method for automatically generating and testing social scientific hypotheses using structural causal models (SCM) and large language models (LLM). The system autonomously generates hypotheses as SCMs by querying an LLM for relevant agents and outcomes, potential causes, and methods to operationalize and measure them. It constructs agents that vary on the exogenous dimensions of the SCM and generates survey questions to gather data about the outcomes from the agents automatically once each simulation is complete. The system determines how the agents should interact using a turn-taking protocol to simulate the conversation. It runs the experiment and gathers the data for analysis.
Results are presented for four social scenarios explored using the system, including bargaining over a mug and a bail hearing. In the bargaining scenario, all three causes had a statistically significant effect on the probability of a deal, with standardized effect sizes estimate with ??*. In the bail hearing scenario, only the defendant's criminal history had a significant effect on the final bail amount, with each additional conviction causing an average increase of $521.53 in bail.
The SCM-based approach offers several advantages, including the automation of hypothesis generation and experimental testing. This allows for the revelation of information not immediately available through direct elicitation. Additionally, assuming or searching for causal structure in data can lead to misidentification, and using SCMs can avoid this problem.
In conclusion, the SCM approach provides an automated method for generating and experimentally testing hypotheses. The simulations conducted using this approach revealed significant causal effects in various scenarios, such as setting bail for a defendant, interviewing for a job as a lawyer, and participating in an auction. The comparison of the results to predictions made by an LLM and auction theory demonstrated the advantages and limitations of using SCMs for data analysis and hypothesis testing. The study focuses on an automated approach to social science using Structural Causal Models (SCMs) and Language Model Models (LLMs), highlighting the importance of avoiding bad controls and misspecifying models when dealing with observational data. The document “Automated Social Science Structural Causal Model Approach” presents a comprehensive analysis of various studies and papers related to the use of large language models (LLMs) in social science research, emphasizing the importance of interpretability and automation in hypothesis generation.
551 word summary
The Automated Social Science Structural Causal Model Approach, developed by Benjamin S. Manning of MIT and Kehang Zhu of Harvard, along with John J. Horton of MIT & NBER, presents an innovative method for automatically generating and testing social scientific hypotheses using structural causal models (SCM) and large language models (LLM). The system autonomously generates hypotheses as SCMs by querying an LLM for relevant agents and outcomes, potential causes, and methods to operationalize and measure them. It constructs agents that vary on the exogenous dimensions of the SCM and generates survey questions to gather data about the outcomes from the agents automatically once each simulation is complete. The system determines how the agents should interact using a turn-taking protocol to simulate the conversation. It runs the experiment and gathers the data for analysis.
Results are presented for four social scenarios explored using the system, including bargaining over a mug and a bail hearing. In the bargaining scenario, all three causes had a statistically significant effect on the probability of a deal, with standardized effect sizes estimate with ??*. In the bail hearing scenario, only the defendant's criminal history had a significant effect on the final bail amount, with each additional conviction causing an average increase of $521.53 in bail.
The SCM-based approach offers several advantages, including the automation of hypothesis generation and experimental testing. This allows for the revelation of information not immediately available through direct elicitation. Additionally, assuming or searching for causal structure in data can lead to misidentification, and using SCMs can avoid this problem.
In conclusion, the SCM approach provides an automated method for generating and experimentally testing hypotheses. The simulations conducted using this approach revealed significant causal effects in various scenarios, such as setting bail for a defendant, interviewing for a job as a lawyer, and participating in an auction. The comparison of the results to predictions made by an LLM and auction theory demonstrated the advantages and limitations of using SCMs for data analysis and hypothesis testing.
The study focuses on an automated approach to social science using Structural Causal Models (SCMs) and Language Model Models (LLMs). The fitted SCMs are unbiased due to randomized experiments, providing unbiased measurements of downstream endogenous outcomes. This allows for the identification of coefficients on the fitted SCM. The study also highlights the importance of knowing the actual causal structure of scenarios, as demonstrated through a comparison of the true and misspecified SCMs. The study emphasizes the significance of avoiding bad controls and misspecifying models when dealing with observational data.
The document “Automated Social Science Structural Causal Model Approach” presents a comprehensive analysis of various studies and papers related to the use of large language models (LLMs) in social science research. It covers a wide range of topics, including cognitive models, generative AI, habit formation, persuasion, and decision-making. It also discusses the potential of LLMs in hypothesis generation and the challenges associated with interpreting the hidden relationships identified by these models.
Overall, the document offers a comprehensive overview of the use of LLMs in social science research and highlights the potential of SCMs as an automated and interpretable approach for hypothesis generation. It provides valuable insights into the challenges and opportunities associated with using LLMs in social science research and emphasizes the importance of interpretability and automation in hypothesis generation.
1574 word summary
Automated Social Science: A Structural Causal Model-Based Approach by Benjamin S. Manning of MIT and Kehang Zhu of Harvard, along with John J. Horton of MIT & NBER, presents a method for automatically generating and testing social scientific hypotheses using structural causal models (SCM) and large language models (LLM). The approach is demonstrated through several scenarios, including negotiation, bail hearing, job interview, and auction, with evidence of proposed causal relationships tested and some findings. The in silico simulation results closely match the predictions of auction theory, but the LLM's clearing price predictions are highly inaccurate. However, the LLM's clearing price predictions are dramatically improved if the model can condition on the fitted SCM. The LLM is good at predicting the signs of estimated effects but cannot reliably predict the magnitudes of those effects, suggesting that explicit social simulation gives the model insight not available purely through direct elicitation.
The paper discusses the importance of efficiently generating models to estimate and explores automated social science hypothesis generation through machine learning. It combines automated hypothesis generation and automated in silico hypotheses testing using LLMs for both purposes. The use of SCMs offers a complete plan for experimental design and estimation. The system is implemented in Python and uses GPT-4 for all LLM queries.
The system autonomously generates hypotheses as SCMs by querying an LLM for relevant agents and outcomes, potential causes, and methods to operationalize and measure them. It constructs agents that vary on the exogenous dimensions of the SCM and generates survey questions to gather data about the outcomes from the agents automatically once each simulation is complete. The system determines how the agents should interact using a turn-taking protocol to simulate the conversation. It runs the experiment and gathers the data for analysis.
Results are presented for four social scenarios explored using the system, including bargaining over a mug and a bail hearing. In the bargaining scenario, all three causes had a statistically significant effect on the probability of a deal, with standardized effect sizes estimate with ??*. In the bail hearing scenario, only the defendant's criminal history had a significant effect on the final bail amount, with each additional conviction causing an average increase of $521.53 in bail.
The paper concludes with a discussion of the advantages of identifying causal structure ex-ante when analyzing data and the problems that arise when trying to determine causal relationships ex-post. It also explains how the system generates SCMs and agents, runs the simulated experiments, and estimates the model.
The Automated Social Science Structural Causal Model Approach (SCM) uses a system that automatically generates and experimentally tests hypotheses. In one scenario, the system simulated a judge setting bail for a defendant who committed tax fraud. The results showed that the number of cases the judge has heard and the defendant's criminal history had a significant effect on the bail amount. In another scenario, a person interviewing for a job as a lawyer was simulated, and the system found that passing the bar exam had the most significant effect on getting the job. Finally, in an auction scenario, the system found that each bidder's maximum budget for the piece of art had a positive and statistically significant effect on the final price.
The system operationalized causes as binary variables, count variables, or continuous variables, and it ran factorial experimental designs for all proposed values of each cause. The simulations revealed that only the applicant passing the bar had a clear causal effect on getting the job as a lawyer. When testing for interactions, none were significant.
The results of these experiments were compared to predictions made by an LLM (Language Model) and auction theory. The LLM's predictions were found to be highly inaccurate compared to those from auction theory. The LLM was also unable to accurately predict the path estimates of the fitted SCM. However, when provided with extensive information to make its predictions, including a fitted SCM, the LLM's predictions improved but were still not as accurate as those made by auction theory.
The SCM-based approach offers several advantages, including the automation of hypothesis generation and experimental testing. This allows for the revelation of information not immediately available through direct elicitation. Additionally, assuming or searching for causal structure in data can lead to misidentification, and using SCMs can avoid this problem.
In conclusion, the SCM approach provides an automated method for generating and experimentally testing hypotheses. The simulations conducted using this approach revealed significant causal effects in various scenarios, such as setting bail for a defendant, interviewing for a job as a lawyer, and participating in an auction. The comparison of the results to predictions made by an LLM and auction theory demonstrated the advantages and limitations of using SCMs for data analysis and hypothesis testing.
The study focuses on an automated approach to social science using Structural Causal Models (SCMs) and Language Model Models (LLMs). The fitted SCMs are unbiased due to randomized experiments, providing unbiased measurements of downstream endogenous outcomes. This allows for the identification of coefficients on the fitted SCM. The study also highlights the importance of knowing the actual causal structure of scenarios, as demonstrated through a comparison of the true and misspecified SCMs. The study emphasizes the significance of avoiding bad controls and misspecifying models when dealing with observational data.
The approach to identifying causal relationships when the underlying structure is unknown involves letting the data speak for itself. This can be achieved by generating all possible SCMs for existing variables and evaluating each model based on some criteria. Another method is to add edges that maximize the criteria greedily, which can be further improved by penalizing the model for complexity and removing edges until the model is optimized. However, it's important to note that the algorithm may incorrectly identify the causal structure in some experiments, as demonstrated in the tax fraud scenario.
The study also discusses the process of querying an LLM for the roles of relevant agents in a scenario-neutral prompt, which allows for the gathering of all necessary information to generate the SCM, run the experiment, and analyze the results. The system constructs SCMs variable-by-variable by querying an LLM for an outcome involving the agents in the social scenario of interest. Each endogenous variable is measured with survey questions, and the system aggregates the answers using a pre-programmed menu of mechanical aggregation methods.
The system also addresses the problem of determining speaking order in multi-agent simulations, highlighting six interaction protocols that provide flexibility and reflect the natural ebb and flow of human conversation. Additionally, a two-tier mechanism is implemented to determine when to stop each simulation, ensuring that conversations do not continue indefinitely. After the experiment, a post-experiment survey is conducted to measure the outcome variable in each simulation.
The study concludes by emphasizing the potential use-cases and benefits of the system, such as providing insights that generalize to the real world and alleviating problems in social science research. It also discusses interactivity, replicability, and future research directions, including determining which attributes to endow an LLM-powered agent and engineering social interactions between LLM agents. The study suggests that there is room for improvement and exploration in implementing the SCM-based approach.
The document "Automated Social Science Structural Causal Model Approach" presents a comprehensive analysis of various studies and papers related to the use of large language models (LLMs) in social science research. The document covers a wide range of topics, including cognitive models, generative AI, habit formation, persuasion, and decision-making. It also discusses the potential of LLMs in hypothesis generation and the challenges associated with interpreting the hidden relationships identified by these models.
The document highlights the use of LLMs in understanding human behavior and decision-making processes. It references studies that explore the application of cognitive psychology to understand LLMs, as well as the use of machine learning to study habit formation, exercise, and hygiene. Additionally, it discusses the potential of LLMs in market research and the early experiments with GPT-4, shedding light on the sparks of artificial general intelligence.
Furthermore, the document addresses the replicability of social science experiments and the challenges faced by political practitioners in predicting which messages persuade the public. It also delves into the implications of outcome variation from hidden "dark methods" in social science research and explores the use of generative AI in shaping the future of human crowdsourcing.
The document emphasizes the importance of automated hypothesis generation using LLMs and discusses the limitations of traditional methods in transforming hidden relationships into human-interpretable features. It highlights the potential of structural causal models (SCMs) in generating novel hypotheses and identifying causal paths between variables. The SCM-based approach is presented as an automated, inexpensive, fast, and interpretable method for transforming information from LLMs into SCMs.
In addition, the document provides insights into the evaluation and alignment of LLMs with a given set of objectives. It discusses the potential of top-down exploration to identify deviations in LLM behavior and align them with specific objectives. The document also addresses the interpretability of hypotheses generated from data and emphasizes the ease of interpretation offered by SCMs compared to traditional methods.
Overall, the document offers a comprehensive overview of the use of LLMs in social science research and highlights the potential of SCMs as an automated and interpretable approach for hypothesis generation. It provides valuable insights into the challenges and opportunities associated with using LLMs in social science research and emphasizes the importance of interpretability and automation in hypothesis generation.