An Application of Causal Forest in Corporate Finance: How Does Financing Affect Investment?

Gulen, Huseyin; Jens, Candace; Page, T. Beau

doi:10.2139/ssrn.3583685

Cited by 14 publications

(8 citation statements)

References 49 publications

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“…According to Athey and Imbens (2016), one can optimize CF algorithm by maximizing the variance of τ(x) rather than minimizing the mean squared error in a regression tree. Therefore, if the variance of τ(x) is maximized during the splitting step, the value of the splitting criteria can be selected as an optimal value (Gulen et al, 2020). Although the variance can increase due to smaller sample sizes, causal estimates can be free of overfitting and bias, as in random forest.…”

Section: Nonparametric Causal Forest Methodsmentioning

confidence: 99%

Exploring modern machine learning methods to improve causal-effect estimation

Kim

Choi

2022

Communications for Statistical Applications and Methods

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This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

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Section: Nonparametric Causal Forest Methodsmentioning

confidence: 99%

Exploring modern machine learning methods to improve causal-effect estimation

Kim

Choi

2022

Communications for Statistical Applications and Methods

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“…We implemented honest causal forests wherein subsamples were randomly split in half – the first half was used when performing splitting while the second half was used to populate the tree’s leaf nodes. By using different subsamples for constructing the tree in the causal forest and for making predictions, we made our model less prone to overfitting [23]. Applying this novel empirical methodology, we were able to examine the causal effects of each treatment on the three outcome variables.…”

Section: Methodsmentioning

confidence: 99%

Longitudinal Analysis of the Determinants of Life Expectancy and Healthy Life Expectancy: A Causal Approach

Aanegola

Nakamura-Sakai

Kumar

2021

Preprint

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Understanding the determinants of health is essential to designing effective strategies to advance economic growth, reduce disease and disability, and enhance quality of life. We undertake a comprehensive outlook on public health by incorporating three metrics - life expectancy (LE), healthy life expectancy (HLE), and the discrepancy between the two. We investigate the effects of various health and socio-economic factors on these metrics and employ causal machine learning and statistical methods such as propensity score matching, X-learners, and causal forests to calculate treatment effects. An increase in basic water services and public health expenditure significantly increased average LE, whereas high HIV prevalence rates and poverty rates reduced average LE. High GNI per capita and moderate BMI increased HLE while high HIV prevalence rates decreased HLE. Moderate BMI and high GNI per capita expand the gap between HLE and LE, whereas high HIV prevalence rates diminish this gap. Results suggest that policymakers should utilize governmental resources to improve public health infrastructure rather than provide fiscal incentives to encourage private healthcare infrastructure. Additionally, more emphasis should be put on increasing educational levels of the general public by increasing educational expenditure and making educational institutions, public and private, more accountable.

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“…Random forests are often used to estimate average causal effects, and Wager and Athey combined random forests and causal trees to define the concept of causal forests [28], which is more applicable to heterogeneous treatment effect estimation problems. Gulen et al [5] introduce causal forests into the study of the effect of the default on investment and demonstrate that causal forests can better handle endogeneity and heterogeneity problems compared with traditional OLS and matching methods.…”

Section: Related Workmentioning

confidence: 99%

Learning counterfactual outcomes of MOOC multiple learning behaviors

Zhang

Huang

et al. 2023

Comp Applic In Engineering

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The absence of counterfactual outcomes presents a fundamental challenge in causal inference. However, existing work typically does not apply to multiple learning behaviors of Massive Open Online Courses. This paper proposes a counterfactual representation learning model based on multitask learning, applicable to any dimension, and any type of treatment. The model consists of a potential outcome network and a propensity score encoder, which shares feature information from the base layer. The propensity scores calculated by the encoder are then utilized in the potential outcome network to mitigate selection bias. Experiments based on real‐world data sets demonstrate the superior performance of our model compared with baselines.

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An Application of Causal Forest in Corporate Finance: How Does Financing Affect Investment?

Cited by 14 publications

References 49 publications

Exploring modern machine learning methods to improve causal-effect estimation

Exploring modern machine learning methods to improve causal-effect estimation

Longitudinal Analysis of the Determinants of Life Expectancy and Healthy Life Expectancy: A Causal Approach

Learning counterfactual outcomes of MOOC multiple learning behaviors

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